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networks</title> <meta name="description" content="Search results for: neural networks"> <meta name="keywords" content="neural networks"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="neural networks"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2396</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: neural networks</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2396</span> A Combined Neural Network Approach to Soccer Player Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wenbin%20Zhang">Wenbin Zhang</a>, <a href="https://publications.waset.org/search?q=Hantian%20Wu"> Hantian Wu</a>, <a href="https://publications.waset.org/search?q=Jian%20Tang"> Jian Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>An artificial neural network is a mathematical model inspired by biological neural networks. There are several kinds of neural networks and they are widely used in many areas, such as: prediction, detection, and classification. Meanwhile, in day to day life, people always have to make many difficult decisions. For example, the coach of a soccer club has to decide which offensive player to be selected to play in a certain game. This work describes a novel Neural Network using a combination of the General Regression Neural Network and the Probabilistic Neural Networks to help a soccer coach make an informed decision.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=General%20Regression%20Neural%20Network" title="General Regression Neural Network">General Regression Neural Network</a>, <a href="https://publications.waset.org/search?q=Probabilistic%20Neural%20Networks" title=" Probabilistic Neural Networks"> Probabilistic Neural Networks</a>, <a href="https://publications.waset.org/search?q=Neural%20function." title=" Neural function."> Neural function.</a> </p> <a href="https://publications.waset.org/10001122/a-combined-neural-network-approach-to-soccer-player-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001122/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001122/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001122/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001122/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001122/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001122/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001122/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001122/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001122/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001122/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3763</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2395</span> The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Radouane%20Iqdour">Radouane Iqdour</a>, <a href="https://publications.waset.org/search?q=Abdelouhab%20Zeroual"> Abdelouhab Zeroual</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribi猫re algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also performed, and the obtained results show that the neural networks are more efficient and gave the best results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Daily%20solar%20radiation" title="Daily solar radiation">Daily solar radiation</a>, <a href="https://publications.waset.org/search?q=Prediction" title=" Prediction"> Prediction</a>, <a href="https://publications.waset.org/search?q=MLP%20neural%0D%0Anetworks" title=" MLP neural networks"> MLP neural networks</a>, <a href="https://publications.waset.org/search?q=linear%20model" title=" linear model"> linear model</a> </p> <a href="https://publications.waset.org/5005/the-multi-layered-perceptrons-neural-networks-for-the-prediction-of-daily-solar-radiation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5005/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5005/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5005/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5005/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5005/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5005/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5005/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5005/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5005/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5005/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1329</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2394</span> A Fast Neural Algorithm for Serial Code Detection in a Stream of Sequential Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hazem%20M.%20El-Bakry">Hazem M. El-Bakry</a>, <a href="https://publications.waset.org/search?q=Qiangfu%20Zhao"> Qiangfu Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fast%20Code%2FData%20Detection" title="Fast Code/Data Detection">Fast Code/Data Detection</a>, <a href="https://publications.waset.org/search?q=Neural%20Networks" title=" Neural Networks"> Neural Networks</a>, <a href="https://publications.waset.org/search?q=Cross%20Correlation" title=" Cross Correlation"> Cross Correlation</a>, <a href="https://publications.waset.org/search?q=real%2Fcomplex%20values." title=" real/complex values."> real/complex values.</a> </p> <a href="https://publications.waset.org/12198/a-fast-neural-algorithm-for-serial-code-detection-in-a-stream-of-sequential-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12198/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12198/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12198/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12198/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12198/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12198/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12198/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12198/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12198/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12198/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1626</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2393</span> Fast Complex Valued Time Delay Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hazem%20M.%20El-Bakry">Hazem M. El-Bakry</a>, <a href="https://publications.waset.org/search?q=Qiangfu%20Zhao"> Qiangfu Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Here, a new idea to speed up the operation of complex valued time delay neural networks is presented. The whole data are collected together in a long vector and then tested as a one input pattern. The proposed fast complex valued time delay neural networks uses cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically that the number of computation steps required for the presented fast complex valued time delay neural networks is less than that needed by classical time delay neural networks. Simulation results using MATLAB confirm the theoretical computations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fast%20Complex%20Valued%20Time%20Delay%20Neural%0ANetworks" title="Fast Complex Valued Time Delay Neural Networks">Fast Complex Valued Time Delay Neural Networks</a>, <a href="https://publications.waset.org/search?q=Cross%20Correlation" title=" Cross Correlation"> Cross Correlation</a>, <a href="https://publications.waset.org/search?q=Frequency%20Domain" title=" Frequency Domain"> Frequency Domain</a> </p> <a href="https://publications.waset.org/3218/fast-complex-valued-time-delay-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3218/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3218/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3218/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3218/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3218/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3218/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3218/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3218/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3218/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3218/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3218.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1825</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2392</span> Application of Wavelet Neural Networks in Optimization of Skeletal Buildings under Frequency Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohammad%20Reza%20Ghasemi">Mohammad Reza Ghasemi</a>, <a href="https://publications.waset.org/search?q=Amin%20Ghorbani"> Amin Ghorbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of the present work is to decrease the computational burden for optimum design of steel frames with frequency constraints using a new type of neural networks called Wavelet Neural Network. It is contested to train a suitable neural network for frequency approximation work as the analysis program. The combination of wavelet theory and Neural Networks (NN) has lead to the development of wavelet neural networks. Wavelet neural networks are feed-forward networks using wavelet as activation function. Wavelets are mathematical functions within suitable inner parameters, which help them to approximate arbitrary functions. WNN was used to predict the frequency of the structures. In WNN a RAtional function with Second order Poles (RASP) wavelet was used as a transfer function. It is shown that the convergence speed was faster than other neural networks. Also comparisons of WNN with the embedded Artificial Neural Network (ANN) and with approximate techniques and also with analytical solutions are available in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Weight%20Minimization" title="Weight Minimization">Weight Minimization</a>, <a href="https://publications.waset.org/search?q=Frequency%20Constraints" title=" Frequency Constraints"> Frequency Constraints</a>, <a href="https://publications.waset.org/search?q=Steel%0AFrames" title=" Steel Frames"> Steel Frames</a>, <a href="https://publications.waset.org/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/search?q=WNN" title=" WNN"> WNN</a>, <a href="https://publications.waset.org/search?q=RASP%20Function." title=" RASP Function."> RASP Function.</a> </p> <a href="https://publications.waset.org/3606/application-of-wavelet-neural-networks-in-optimization-of-skeletal-buildings-under-frequency-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3606/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3606/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3606/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3606/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3606/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3606/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3606/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3606/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3606/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3606/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3606.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1740</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2391</span> Diagnosis of Ovarian Cancer with Proteomic Patterns in Serum using Independent Component Analysis and Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Simone%20C.%20F.%20Neves">Simone C. F. Neves</a>, <a href="https://publications.waset.org/search?q=L%C3%BAcio%20F.%20A.%20Campos"> L煤cio F. A. Campos</a>, <a href="https://publications.waset.org/search?q=Ewaldo%20Santana"> Ewaldo Santana</a>, <a href="https://publications.waset.org/search?q=Ginalber%20L.%20O.%20Serra"> Ginalber L. O. Serra</a>, <a href="https://publications.waset.org/search?q=Allan%20K.%20Barros"> Allan K. Barros</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a method for discrimination and classification of ovarian with benign, malignant and normal tissue using independent component analysis and neural networks. The method was tested for a proteomic patters set from A database, and radial basis functions neural networks. The best performance was obtained with probabilistic neural networks, resulting I 99% success rate, with 98% of specificity e 100% of sensitivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cancer%20ovarian" title="Cancer ovarian">Cancer ovarian</a>, <a href="https://publications.waset.org/search?q=Proteomic%20patterns%20in%20serum" title=" Proteomic patterns in serum"> Proteomic patterns in serum</a>, <a href="https://publications.waset.org/search?q=independent%20component%20analysis%20and%20neural%20networks." title=" independent component analysis and neural networks."> independent component analysis and neural networks.</a> </p> <a href="https://publications.waset.org/12668/diagnosis-of-ovarian-cancer-with-proteomic-patterns-in-serum-using-independent-component-analysis-and-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12668/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12668/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12668/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12668/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12668/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12668/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12668/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12668/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12668/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12668/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12668.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1831</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2390</span> Analysis of Periodic Solution of Delay Fuzzy BAM Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Qianhong%20Zhang">Qianhong Zhang</a>, <a href="https://publications.waset.org/search?q=Lihui%20Yang"> Lihui Yang</a>, <a href="https://publications.waset.org/search?q=Daixi%20Liao"> Daixi Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, by employing a new Lyapunov functional and an elementary inequality analysis technique, some sufficient conditions are derived to ensure the existence and uniqueness of periodic oscillatory solution for fuzzy bi-directional memory (BAM) neural networks with time-varying delays, and all other solutions of the fuzzy BAM neural networks converge the uniqueness periodic solution. These criteria are presented in terms of system parameters and have important leading significance in the design and applications of neural networks. Moreover an example is given to illustrate the effectiveness and feasible of results obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fuzzy%20BAM%20neural%20networks" title="Fuzzy BAM neural networks">Fuzzy BAM neural networks</a>, <a href="https://publications.waset.org/search?q=Periodic%20solution" title=" Periodic solution"> Periodic solution</a>, <a href="https://publications.waset.org/search?q=Global%20exponential%20stability" title="Global exponential stability">Global exponential stability</a>, <a href="https://publications.waset.org/search?q=Time-varying%20delays" title=" Time-varying delays"> Time-varying delays</a> </p> <a href="https://publications.waset.org/1889/analysis-of-periodic-solution-of-delay-fuzzy-bam-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1889/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1889/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1889/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1889/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1889/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1889/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1889/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1889/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1889/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1889/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1515</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2389</span> A Modified Cross Correlation in the Frequency Domain for Fast Pattern Detection Using Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hazem%20M.%20El-Bakry">Hazem M. El-Bakry</a>, <a href="https://publications.waset.org/search?q=Qiangfu%20Zhao"> Qiangfu Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, neural networks have shown good results for detection of a certain pattern in a given image. In our previous papers [1-5], a fast algorithm for pattern detection using neural networks was presented. Such algorithm was designed based on cross correlation in the frequency domain between the input image and the weights of neural networks. Image conversion into symmetric shape was established so that fast neural networks can give the same results as conventional neural networks. Another configuration of symmetry was suggested in [3,4] to improve the speed up ratio. In this paper, our previous algorithm for fast neural networks is developed. The frequency domain cross correlation is modified in order to compensate for the symmetric condition which is required by the input image. Two new ideas are introduced to modify the cross correlation algorithm. Both methods accelerate the speed of the fast neural networks as there is no need for converting the input image into symmetric one as previous. Theoretical and practical results show that both approaches provide faster speed up ratio than the previous algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fast%20Pattern%20Detection" title="Fast Pattern Detection">Fast Pattern Detection</a>, <a href="https://publications.waset.org/search?q=Neural%20Networks" title=" Neural Networks"> Neural Networks</a>, <a href="https://publications.waset.org/search?q=Modified%20Cross%20Correlation" title=" Modified Cross Correlation"> Modified Cross Correlation</a> </p> <a href="https://publications.waset.org/2525/a-modified-cross-correlation-in-the-frequency-domain-for-fast-pattern-detection-using-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2525/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2525/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2525/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2525/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2525/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2525/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2525/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2525/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2525/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2525/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1745</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2388</span> Fast Object/Face Detection Using Neural Networks and Fast Fourier Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hazem%20M.%20El-Bakry">Hazem M. El-Bakry</a>, <a href="https://publications.waset.org/search?q=Qiangfu%20Zhao"> Qiangfu Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, fast neural networks for object/face detection were presented in [1-3]. The speed up factor of these networks relies on performing cross correlation in the frequency domain between the input image and the weights of the hidden layer. But, these equations given in [1-3] for conventional and fast neural networks are not valid for many reasons presented here. In this paper, correct equations for cross correlation in the spatial and frequency domains are presented. Furthermore, correct formulas for the number of computation steps required by conventional and fast neural networks given in [1-3] are introduced. A new formula for the speed up ratio is established. Also, corrections for the equations of fast multi scale object/face detection are given. Moreover, commutative cross correlation is achieved. Simulation results show that sub-image detection based on cross correlation in the frequency domain is faster than classical neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Conventional%20Neural%20Networks" title="Conventional Neural Networks">Conventional Neural Networks</a>, <a href="https://publications.waset.org/search?q=Fast%20Neural%0ANetworks" title=" Fast Neural Networks"> Fast Neural Networks</a>, <a href="https://publications.waset.org/search?q=Cross%20Correlation%20in%20the%20Frequency%20Domain." title=" Cross Correlation in the Frequency Domain."> Cross Correlation in the Frequency Domain.</a> </p> <a href="https://publications.waset.org/3384/fast-objectface-detection-using-neural-networks-and-fast-fourier-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3384/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3384/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3384/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3384/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3384/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3384/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3384/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3384/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3384/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3384/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3384.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2480</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2387</span> Sub-Image Detection Using Fast Neural Processors and Image Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hazem%20M.%20El-Bakry">Hazem M. El-Bakry</a>, <a href="https://publications.waset.org/search?q=Qiangfu%20Zhao"> Qiangfu Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fast%20Neural%20Networks" title="Fast Neural Networks">Fast Neural Networks</a>, <a href="https://publications.waset.org/search?q=2D-FFT" title=" 2D-FFT"> 2D-FFT</a>, <a href="https://publications.waset.org/search?q=CrossCorrelation" title=" CrossCorrelation"> CrossCorrelation</a>, <a href="https://publications.waset.org/search?q=Image%20decomposition" title=" Image decomposition"> Image decomposition</a>, <a href="https://publications.waset.org/search?q=Parallel%20Processing." title=" Parallel Processing."> Parallel Processing.</a> </p> <a href="https://publications.waset.org/12561/sub-image-detection-using-fast-neural-processors-and-image-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12561/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12561/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12561/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12561/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12561/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12561/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12561/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12561/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12561/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12561/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12561.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2179</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2386</span> Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Chaouachi">A. Chaouachi</a>, <a href="https://publications.waset.org/search?q=R.M.%20Kamel"> R.M. Kamel</a>, <a href="https://publications.waset.org/search?q=R.%20Ichikawa"> R. Ichikawa</a>, <a href="https://publications.waset.org/search?q=H.%20Hayashi"> H. Hayashi</a>, <a href="https://publications.waset.org/search?q=K.%20Nagasaka"> K. Nagasaka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Neural%20network%20ensemble" title="Neural network ensemble">Neural network ensemble</a>, <a href="https://publications.waset.org/search?q=Solar%20power%20generation" title=" Solar power generation"> Solar power generation</a>, <a href="https://publications.waset.org/search?q=24%20hour%20forecasting" title="24 hour forecasting">24 hour forecasting</a>, <a href="https://publications.waset.org/search?q=Comparative%20study" title=" Comparative study"> Comparative study</a> </p> <a href="https://publications.waset.org/8446/neural-network-ensemble-based-solar-power-generation-short-term-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8446/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8446/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8446/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8446/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8446/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8446/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8446/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8446/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8446/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8446/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8446.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3277</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2385</span> Investigation of Some Technical Indexes inStock Forecasting Using Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Myungsook%20Klassen">Myungsook Klassen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine its effectiveness as inputs. The feed forward neural network of Levenberg-Marquardt algorithm is applied to perform one step ahead forecasting of NASDAQ and Dow stock prices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Stock%20Market%20Prediction" title="Stock Market Prediction">Stock Market Prediction</a>, <a href="https://publications.waset.org/search?q=Neural%20Networks" title=" Neural Networks"> Neural Networks</a>, <a href="https://publications.waset.org/search?q=Levenberg-Marquadt%20Algorithm" title="Levenberg-Marquadt Algorithm">Levenberg-Marquadt Algorithm</a>, <a href="https://publications.waset.org/search?q=Technical%20Indexes" title=" Technical Indexes"> Technical Indexes</a> </p> <a href="https://publications.waset.org/5034/investigation-of-some-technical-indexes-instock-forecasting-using-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5034/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5034/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5034/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5034/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5034/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5034/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5034/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5034/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5034/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5034/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5034.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1947</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2384</span> Using Artificial Neural Networks for Optical Imaging of Fluorescent Biomarkers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=K.%20A.%20Laptinskiy">K. A. Laptinskiy</a>, <a href="https://publications.waset.org/search?q=S.%20A.%20Burikov"> S. A. Burikov</a>, <a href="https://publications.waset.org/search?q=A.%20M.%20Vervald"> A. M. Vervald</a>, <a href="https://publications.waset.org/search?q=S.%20A.%20Dolenko"> S. A. Dolenko</a>, <a href="https://publications.waset.org/search?q=T.%20A.%20Dolenko"> T. A. Dolenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20networks" title="Artificial neural networks">Artificial neural networks</a>, <a href="https://publications.waset.org/search?q=fluorescence" title=" fluorescence"> fluorescence</a>, <a href="https://publications.waset.org/search?q=data%0D%0Aaggregation." title=" data aggregation."> data aggregation.</a> </p> <a href="https://publications.waset.org/9999549/using-artificial-neural-networks-for-optical-imaging-of-fluorescent-biomarkers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999549/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999549/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999549/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999549/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999549/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999549/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999549/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999549/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999549/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999549/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999549.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2109</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2383</span> Novel Approach for Promoting the Generalization Ability of Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Naiqin%20Feng">Naiqin Feng</a>, <a href="https://publications.waset.org/search?q=Fang%20Wang"> Fang Wang</a>, <a href="https://publications.waset.org/search?q=Yuhui%20Qiu"> Yuhui Qiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new approach to promote the generalization ability of neural networks is presented. It is based on the point of view of fuzzy theory. This approach is implemented through shrinking or magnifying the input vector, thereby reducing the difference between training set and testing set. It is called 鈥渟hrinking-magnifying approach" (SMA). At the same time, a new algorithm; 伪-algorithm is presented to find out the appropriate shrinking-magnifying-factor (SMF) 伪 and obtain better generalization ability of neural networks. Quite a few simulation experiments serve to study the effect of SMA and 伪-algorithm. The experiment results are discussed in detail, and the function principle of SMA is analyzed in theory. The results of experiments and analyses show that the new approach is not only simpler and easier, but also is very effective to many neural networks and many classification problems. In our experiments, the proportions promoting the generalization ability of neural networks have even reached 90%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fuzzy%20theory" title="Fuzzy theory">Fuzzy theory</a>, <a href="https://publications.waset.org/search?q=generalization" title=" generalization"> generalization</a>, <a href="https://publications.waset.org/search?q=misclassification%20rate" title=" misclassification rate"> misclassification rate</a>, <a href="https://publications.waset.org/search?q=neural%20network." title="neural network.">neural network.</a> </p> <a href="https://publications.waset.org/14230/novel-approach-for-promoting-the-generalization-ability-of-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14230/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14230/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14230/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14230/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14230/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14230/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14230/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14230/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14230/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14230/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1535</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2382</span> Accelerating Integer Neural Networks On Low Cost DSPs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Thomas%20Behan">Thomas Behan</a>, <a href="https://publications.waset.org/search?q=Zaiyi%20Liao"> Zaiyi Liao</a>, <a href="https://publications.waset.org/search?q=Lian%20Zhao"> Lian Zhao</a>, <a href="https://publications.waset.org/search?q=Chunting%20Yang"> Chunting Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, low end Digital Signal Processors (DSPs) are applied to accelerate integer neural networks. The use of DSPs to accelerate neural networks has been a topic of study for some time, and has demonstrated significant performance improvements. Recently, work has been done on integer only neural networks, which greatly reduces hardware requirements, and thus allows for cheaper hardware implementation. DSPs with Arithmetic Logic Units (ALUs) that support floating or fixed point arithmetic are generally more expensive than their integer only counterparts due to increased circuit complexity. However if the need for floating or fixed point math operation can be removed, then simpler, lower cost DSPs can be used. To achieve this, an integer only neural network is created in this paper, which is then accelerated by using DSP instructions to improve performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Digital%20Signal%20Processor%20%28DSP%29" title="Digital Signal Processor (DSP)">Digital Signal Processor (DSP)</a>, <a href="https://publications.waset.org/search?q=Integer%20Neural%20Network%28INN%29" title=" Integer Neural Network(INN)"> Integer Neural Network(INN)</a>, <a href="https://publications.waset.org/search?q=Low%20Cost%20Neural%20Network" title=" Low Cost Neural Network"> Low Cost Neural Network</a>, <a href="https://publications.waset.org/search?q=Integer%20Neural%20Network%20DSPImplementation." title=" Integer Neural Network DSPImplementation."> Integer Neural Network DSPImplementation.</a> </p> <a href="https://publications.waset.org/2049/accelerating-integer-neural-networks-on-low-cost-dsps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2049/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2049/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2049/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2049/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2049/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2049/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2049/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2049/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2049/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2049/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1796</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2381</span> Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2DFunctions Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wajdi%20Bellil">Wajdi Bellil</a>, <a href="https://publications.waset.org/search?q=Chokri%20Ben%20Amar"> Chokri Ben Amar</a>, <a href="https://publications.waset.org/search?q=Adel%20M.%20Alimi"> Adel M. Alimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate Ôêºf as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Beta%20wavelets%20networks" title="Beta wavelets networks">Beta wavelets networks</a>, <a href="https://publications.waset.org/search?q=RBF%20neural%20network" title=" RBF neural network"> RBF neural network</a>, <a href="https://publications.waset.org/search?q=training%20algorithms" title="training algorithms">training algorithms</a>, <a href="https://publications.waset.org/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/search?q=1-D" title=" 1-D"> 1-D</a>, <a href="https://publications.waset.org/search?q=2D%20function%20approximation." title=" 2D function approximation."> 2D function approximation.</a> </p> <a href="https://publications.waset.org/2132/comparison-between-beta-wavelets-neural-networks-rbf-neural-networks-and-polynomial-approximation-for-1d-2dfunctions-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2132/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2132/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2132/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2132/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2132/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2132/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2132/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2132/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2132/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2132/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2132.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1919</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2380</span> Application of Artificial Neural Networks for Temperature Forecasting </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohsen%20Hayati">Mohsen Hayati</a>, <a href="https://publications.waset.org/search?q=Zahra%20Mohebi"> Zahra Mohebi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20networks" title="Artificial neural networks">Artificial neural networks</a>, <a href="https://publications.waset.org/search?q=Forecasting" title=" Forecasting"> Forecasting</a>, <a href="https://publications.waset.org/search?q=Weather" title=" Weather"> Weather</a>, <a href="https://publications.waset.org/search?q=Multi-layer%20perceptron." title="Multi-layer perceptron.">Multi-layer perceptron.</a> </p> <a href="https://publications.waset.org/8486/application-of-artificial-neural-networks-for-temperature-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8486/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8486/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8486/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8486/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8486/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8486/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8486/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8486/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8486/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8486/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8486.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4357</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2379</span> Exponential Stability Analysis for Switched Cellular Neural Networks with Time-varying Delays and Impulsive Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Zixin%20Liu">Zixin Liu</a>, <a href="https://publications.waset.org/search?q=Fangwei%20Chen"> Fangwei Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this Letter, a class of impulsive switched cellular neural networks with time-varying delays is investigated. At the same time, parametric uncertainties assumed to be norm bounded are considered. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions guaranteeing exponential stability for all admissible parametric uncertainties are derived via constructing appropriate Lyapunov functional. One numerical example is provided to illustrate the validity of the main results obtained in this paper.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Switched%20systems" title="Switched systems">Switched systems</a>, <a href="https://publications.waset.org/search?q=exponential%20stability" title=" exponential stability"> exponential stability</a>, <a href="https://publications.waset.org/search?q=cellular%20neural%20networks." title=" cellular neural networks."> cellular neural networks.</a> </p> <a href="https://publications.waset.org/6423/exponential-stability-analysis-for-switched-cellular-neural-networks-with-time-varying-delays-and-impulsive-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6423/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6423/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6423/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6423/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6423/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6423/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6423/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6423/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6423/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6423/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6423.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1417</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2378</span> A Novel Fuzzy-Neural Based Medical Diagnosis System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Moein">S. Moein</a>, <a href="https://publications.waset.org/search?q=S.%20A.%20Monadjemi"> S. A. Monadjemi</a>, <a href="https://publications.waset.org/search?q=P.%20Moallem"> P. Moallem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, application of artificial neural networks in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and applied to a MLP neural network. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. Outcomes suggest the role of effective symptoms selection and the advantages of data fuzzificaton on a neural networks-based automatic medical diagnosis system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20Neural%20Networks" title="Artificial Neural Networks">Artificial Neural Networks</a>, <a href="https://publications.waset.org/search?q=Fuzzy%20Logic" title=" Fuzzy Logic"> Fuzzy Logic</a>, <a href="https://publications.waset.org/search?q=MedicalDiagnosis" title=" MedicalDiagnosis"> MedicalDiagnosis</a>, <a href="https://publications.waset.org/search?q=Symptoms" title=" Symptoms"> Symptoms</a>, <a href="https://publications.waset.org/search?q=Fuzzification." title=" Fuzzification."> Fuzzification.</a> </p> <a href="https://publications.waset.org/15035/a-novel-fuzzy-neural-based-medical-diagnosis-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15035/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15035/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15035/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15035/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15035/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15035/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15035/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15035/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15035/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15035/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15035.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2260</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2377</span> Testing the Accuracy of ML-ANN for Harmonic Estimation in Balanced Industrial Distribution Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wael%20M.%20El-Mamlouk">Wael M. El-Mamlouk</a>, <a href="https://publications.waset.org/search?q=Metwally%20A.%20El-Sharkawy"> Metwally A. El-Sharkawy</a>, <a href="https://publications.waset.org/search?q=Hossam.%20E.%20Mostafa"> Hossam. E. Mostafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyze and test a scheme for the estimation of electrical fundamental frequency signals from the harmonic load current and voltage signals. The scheme was based on using two different Multi Layer Artificial Neural Networks (ML-ANN) one for the current and the other for the voltage. This study also analyzes and tests the effect of choosing the optimum artificial neural networks- sizes which determine the quality and accuracy of the estimation of electrical fundamental frequency signals. The simulink tool box of the Matlab program for the simulation of the test system and the test of the neural networks has been used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Harmonics" title="Harmonics">Harmonics</a>, <a href="https://publications.waset.org/search?q=Neural%20Networks" title=" Neural Networks"> Neural Networks</a>, <a href="https://publications.waset.org/search?q=Modeling" title=" Modeling"> Modeling</a>, <a href="https://publications.waset.org/search?q=Simulation" title=" Simulation"> Simulation</a>, <a href="https://publications.waset.org/search?q=Active%20filters" title=" Active filters"> Active filters</a>, <a href="https://publications.waset.org/search?q=electric%20Networks." title=" electric Networks."> electric Networks.</a> </p> <a href="https://publications.waset.org/9322/testing-the-accuracy-of-ml-ann-for-harmonic-estimation-in-balanced-industrial-distribution-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9322/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9322/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9322/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9322/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9322/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9322/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9322/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9322/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9322/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9322/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1594</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2376</span> Prediction of Bath Temperature Using Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=H.%20Meradi">H. Meradi</a>, <a href="https://publications.waset.org/search?q=S.%20Bouhouche"> S. Bouhouche</a>, <a href="https://publications.waset.org/search?q=M.%20Lahreche"> M. Lahreche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=LD%20converter" title="LD converter">LD converter</a>, <a href="https://publications.waset.org/search?q=bath%20temperature" title=" bath temperature"> bath temperature</a>, <a href="https://publications.waset.org/search?q=neural%20networks." title=" neural networks."> neural networks.</a> </p> <a href="https://publications.waset.org/5627/prediction-of-bath-temperature-using-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5627/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5627/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5627/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5627/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5627/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5627/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5627/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5627/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5627/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5627/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5627.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1837</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2375</span> Applications of Cascade Correlation Neural Networks for Cipher System Identification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=B.%20Chandra">B. Chandra</a>, <a href="https://publications.waset.org/search?q=P.%20Paul%20Varghese"> P. Paul Varghese</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Back%20Propagation%20Neural%20Networks" title="Back Propagation Neural Networks">Back Propagation Neural Networks</a>, <a href="https://publications.waset.org/search?q=CascadeCorrelation%20Neural%20Network" title=" CascadeCorrelation Neural Network"> CascadeCorrelation Neural Network</a>, <a href="https://publications.waset.org/search?q=Crypto%20systems" title=" Crypto systems"> Crypto systems</a>, <a href="https://publications.waset.org/search?q=Block%20Cipher" title=" Block Cipher"> Block Cipher</a>, <a href="https://publications.waset.org/search?q=StreamCipher." title=" StreamCipher."> StreamCipher.</a> </p> <a href="https://publications.waset.org/11895/applications-of-cascade-correlation-neural-networks-for-cipher-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11895/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11895/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11895/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11895/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11895/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11895/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11895/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11895/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11895/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11895/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11895.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2444</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2374</span> Improvement of Ground Truth Data for Eye Location on Infrared Driver Recordings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sorin%20Valcan">Sorin Valcan</a>, <a href="https://publications.waset.org/search?q=Mihail%20G%C4%83ianu"> Mihail G膬ianu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need of labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to a ground truth data generation algorithm for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual labels adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Labeling%20automation" title="Labeling automation">Labeling automation</a>, <a href="https://publications.waset.org/search?q=infrared%20camera" title=" infrared camera"> infrared camera</a>, <a href="https://publications.waset.org/search?q=driver%0D%0Amonitoring" title=" driver monitoring"> driver monitoring</a>, <a href="https://publications.waset.org/search?q=eye%20detection" title=" eye detection"> eye detection</a>, <a href="https://publications.waset.org/search?q=Convolutional%20Neural%20Networks." title=" Convolutional Neural Networks."> Convolutional Neural Networks.</a> </p> <a href="https://publications.waset.org/10012870/improvement-of-ground-truth-data-for-eye-location-on-infrared-driver-recordings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012870/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012870/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012870/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012870/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012870/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012870/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012870/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012870/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012870/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012870/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012870.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2373</span> Global Exponential Stability of Impulsive BAM Fuzzy Cellular Neural Networks with Time Delays in the Leakage Terms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Liping%20Zhang">Liping Zhang</a>, <a href="https://publications.waset.org/search?q=Kelin%20Li"> Kelin Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, a class of impulsive BAM fuzzy cellular neural networks with time delays in the leakage terms is formulated and investigated. By establishing a delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM fuzzy cellular neural networks with time delays in the leakage terms are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM fuzzy cellular neural networks. An example is given to show the effectiveness of the results obtained here.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Global%20exponential%20stability" title="Global exponential stability">Global exponential stability</a>, <a href="https://publications.waset.org/search?q=bidirectional%20associative%20memory" title=" bidirectional associative memory"> bidirectional associative memory</a>, <a href="https://publications.waset.org/search?q=fuzzy%20cellular%20neural%20networks" title=" fuzzy cellular neural networks"> fuzzy cellular neural networks</a>, <a href="https://publications.waset.org/search?q=leakage%20delays" title=" leakage delays"> leakage delays</a>, <a href="https://publications.waset.org/search?q=impulses." title=" impulses."> impulses.</a> </p> <a href="https://publications.waset.org/15326/global-exponential-stability-of-impulsive-bam-fuzzy-cellular-neural-networks-with-time-delays-in-the-leakage-terms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15326/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15326/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15326/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15326/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15326/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15326/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15326/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15326/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15326/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15326/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15326.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1330</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2372</span> Almost Periodic Solution for an Impulsive Neural Networks with Distributed Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Lili%20Wang">Lili Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>By using the estimation of the Cauchy matrix of linear impulsive differential equations and Banach fixed point theorem as well as Gronwall-Bellman’s inequality, some sufficient conditions are obtained for the existence and exponential stability of almost periodic solution for an impulsive neural networks with distributed delays. An example is presented to illustrate the feasibility and  effectiveness of the results.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Almost%20periodic%20solution" title="Almost periodic solution">Almost periodic solution</a>, <a href="https://publications.waset.org/search?q=Exponential%20stability" title=" Exponential stability"> Exponential stability</a>, <a href="https://publications.waset.org/search?q=Neural%20networks" title=" Neural networks"> Neural networks</a>, <a href="https://publications.waset.org/search?q=Impulses." title=" Impulses."> Impulses.</a> </p> <a href="https://publications.waset.org/17002/almost-periodic-solution-for-an-impulsive-neural-networks-with-distributed-delays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/17002/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/17002/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/17002/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/17002/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/17002/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/17002/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/17002/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/17002/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/17002/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/17002/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/17002.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1615</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2371</span> Delay-Distribution-Dependent Stability Criteria for BAM Neural Networks with Time-Varying Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J.H.%20Park">J.H. Park</a>, <a href="https://publications.waset.org/search?q=S.%20Lakshmanan"> S. Lakshmanan</a>, <a href="https://publications.waset.org/search?q=H.Y.%20Jung"> H.Y. Jung</a>, <a href="https://publications.waset.org/search?q=S.M.%20Lee"> S.M. Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is concerned with the delay-distributiondependent stability criteria for bidirectional associative memory (BAM) neural networks with time-varying delays. Based on the Lyapunov-Krasovskii functional and stochastic analysis approach, a delay-probability-distribution-dependent sufficient condition is derived to achieve the globally asymptotically mean square stable of the considered BAM neural networks. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulation is given to demonstrate the usefulness and effectiveness of the proposed results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=BAM%20neural%20networks" title="BAM neural networks">BAM neural networks</a>, <a href="https://publications.waset.org/search?q=Probabilistic%20time-varying%20delays" title=" Probabilistic time-varying delays"> Probabilistic time-varying delays</a>, <a href="https://publications.waset.org/search?q=Stability%20criteria." title=" Stability criteria."> Stability criteria.</a> </p> <a href="https://publications.waset.org/3117/delay-distribution-dependent-stability-criteria-for-bam-neural-networks-with-time-varying-delays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3117/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3117/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3117/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3117/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3117/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3117/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3117/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3117/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3117/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3117/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3117.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1418</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2370</span> A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Guang%20Zhou">Guang Zhou</a>, <a href="https://publications.waset.org/search?q=Shouming%20Zhong"> Shouming Zhong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Neural%20networks" title="Neural networks">Neural networks</a>, <a href="https://publications.waset.org/search?q=Delayed%20systems" title=" Delayed systems"> Delayed systems</a>, <a href="https://publications.waset.org/search?q=Lyapunov%20function" title=" Lyapunov function"> Lyapunov function</a>, <a href="https://publications.waset.org/search?q=Stability%20analysis." title=" Stability analysis."> Stability analysis.</a> </p> <a href="https://publications.waset.org/16769/a-new-robust-stability-criterion-for-dynamical-neural-networks-with-mixed-time-delays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16769/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16769/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16769/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16769/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16769/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16769/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16769/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16769/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16769/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16769/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16769.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1584</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2369</span> Robotic Arm Control with Neural Networks Using Genetic Algorithm Optimization Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Pajaziti">A. Pajaziti</a>, <a href="https://publications.waset.org/search?q=H.%20Cana"> H. Cana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Robotic%20Arm" title="Robotic Arm">Robotic Arm</a>, <a href="https://publications.waset.org/search?q=Neural%20Network" title=" Neural Network"> Neural Network</a>, <a href="https://publications.waset.org/search?q=Genetic%20Algorithm" title=" Genetic Algorithm"> Genetic Algorithm</a>, <a href="https://publications.waset.org/search?q=Optimization." title=" Optimization."> Optimization.</a> </p> <a href="https://publications.waset.org/9999081/robotic-arm-control-with-neural-networks-using-genetic-algorithm-optimization-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999081/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999081/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999081/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999081/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999081/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999081/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999081/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999081/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999081/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999081/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999081.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3595</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2368</span> Some Remarkable Properties of a Hopfield Neural Network with Time Delay</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Kelvin%20Rozier">Kelvin Rozier</a>, <a href="https://publications.waset.org/search?q=Vladimir%20E.%20Bondarenko"> Vladimir E. Bondarenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is known that an analog Hopfield neural network with time delay can generate the outputs which are similar to the human electroencephalogram. To gain deeper insights into the mechanisms of rhythm generation by the Hopfield neural networks and to study the effects of noise on their activities, we investigated the behaviors of the networks with symmetric and asymmetric interneuron connections. The neural network under the study consists of 10 identical neurons. For symmetric (fully connected) networks all interneuron connections aij = +1; the interneuron connections for asymmetric networks form an upper triangular matrix with non-zero entries aij = +1. The behavior of the network is described by 10 differential equations, which are solved numerically. The results of simulations demonstrate some remarkable properties of a Hopfield neural network, such as linear growth of outputs, dependence of synchronization properties on the connection type, huge amplification of oscillation by the external uniform noise, and the capability of the neural network to transform one type of noise to another. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Chaos" title="Chaos">Chaos</a>, <a href="https://publications.waset.org/search?q=Hopfield%20neural%20network" title=" Hopfield neural network"> Hopfield neural network</a>, <a href="https://publications.waset.org/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/search?q=synchronization" title=" synchronization"> synchronization</a> </p> <a href="https://publications.waset.org/2754/some-remarkable-properties-of-a-hopfield-neural-network-with-time-delay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2754/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2754/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2754/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2754/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2754/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2754/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2754/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2754/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2754/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2754/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2754.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1890</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2367</span> A Novel Approach to Positive Almost Periodic Solution of BAM Neural Networks with Time-Varying Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Lili%20Wang">Lili Wang</a>, <a href="https://publications.waset.org/search?q=Meng%20Hu"> Meng Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, based on almost periodic functional hull theory and M-matrix theory, some sufficient conditions are established for the existence and uniqueness of positive almost periodic solution for a class of BAM neural networks with time-varying delays. An example is given to illustrate the main results.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Delayed%20BAM%20neural%20networks" title="Delayed BAM neural networks">Delayed BAM neural networks</a>, <a href="https://publications.waset.org/search?q=Hull%20theorem" title=" Hull theorem"> Hull theorem</a>, <a href="https://publications.waset.org/search?q=Mmatrix" title=" Mmatrix"> Mmatrix</a>, <a href="https://publications.waset.org/search?q=Almost%20periodic%20solution" title=" Almost periodic solution"> Almost periodic solution</a>, <a href="https://publications.waset.org/search?q=Global%20exponential%20stability." title=" Global exponential stability."> Global exponential stability.</a> </p> <a href="https://publications.waset.org/10963/a-novel-approach-to-positive-almost-periodic-solution-of-bam-neural-networks-with-time-varying-delays" class="btn btn-primary btn-sm">Procedia</a> <a 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