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network algorithm</title> <meta name="description" content="Search results for: neural network algorithm"> <meta name="keywords" content="neural network algorithm"> <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 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action="https://publications.waset.org/abstracts/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 network algorithm"> <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> 8045</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: neural network algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8045</span> Selecting the Best RBF Neural Network Using PSO Algorithm for ECG Signal Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Mohsenifar">Najmeh Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Narjes%20Mohsenifar"> Narjes Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Kargar"> Abbas Kargar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20artificial%20neural%20network" title=" RBF artificial neural network"> RBF artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO%20algorithm" title=" PSO algorithm"> PSO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=predict" title=" predict"> predict</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a> </p> <a href="https://publications.waset.org/abstracts/33466/selecting-the-best-rbf-neural-network-using-pso-algorithm-for-ecg-signal-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33466.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">626</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8044</span> Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelhadi%20Lotfi">Abdelhadi Lotfi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20neural%20networks" title=" probabilistic neural networks"> probabilistic neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20optimization" title=" network optimization"> network optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/104139/optimizing-the-probabilistic-neural-network-training-algorithm-for-multi-class-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104139.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">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8043</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/abstracts/search?q=Arbnor%20Pajaziti">Arbnor Pajaziti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Cana"> Hasan Cana</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotic%20arm" title="robotic arm">robotic arm</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/7408/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/abstracts/7408.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">523</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8042</span> A Model for Diagnosis and Prediction of Coronavirus Using Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajjad%20Baghernezhad">Sajjad Baghernezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title="COVID-19">COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20technique" title=" decision support technique"> decision support technique</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=memetic%20algorithm" title=" memetic algorithm"> memetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/168424/a-model-for-diagnosis-and-prediction-of-coronavirus-using-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168424.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">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8041</span> Diesel Fault Prediction Based on Optimized Gray Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Han%20Bing">Han Bing</a>, <a href="https://publications.waset.org/abstracts/search?q=Yin%20Zhenjie"> Yin Zhenjie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20prediction" title="fault prediction">fault prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=GM%281" title=" GM(1"> GM(1</a>, <a href="https://publications.waset.org/abstracts/search?q=5%29%20genetic%20algorithm" title="5) genetic algorithm">5) genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=GBPGA" title=" GBPGA"> GBPGA</a> </p> <a href="https://publications.waset.org/abstracts/48844/diesel-fault-prediction-based-on-optimized-gray-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48844.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">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8040</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15843.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">563</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8039</span> Person Re-Identification using Siamese Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sello%20Mokwena">Sello Mokwena</a>, <a href="https://publications.waset.org/abstracts/search?q=Monyepao%20Thabang"> Monyepao Thabang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20network" title="camera network">camera network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20topology" title=" convolutional neural network topology"> convolutional neural network topology</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20tracking" title=" person tracking"> person tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese" title=" siamese"> siamese</a> </p> <a href="https://publications.waset.org/abstracts/171989/person-re-identification-using-siamese-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171989.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">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8038</span> Improving the Performance of Back-Propagation Training Algorithm by Using ANN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Pratap%20Singh%20Kirar">Vishnu Pratap Singh Kirar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20minima" title=" local minima"> local minima</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20convergence%20rate" title=" fast convergence rate"> fast convergence rate</a> </p> <a href="https://publications.waset.org/abstracts/22746/improving-the-performance-of-back-propagation-training-algorithm-by-using-ann" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22746.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">498</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8037</span> A t-SNE and UMAP Based Neural Network Image Classification Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shelby%20Simpson">Shelby Simpson</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20Stanley"> William Stanley</a>, <a href="https://publications.waset.org/abstracts/search?q=Namir%20Naba"> Namir Naba</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaodi%20Wang"> Xiaodi Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=t-SNE" title="t-SNE">t-SNE</a>, <a href="https://publications.waset.org/abstracts/search?q=UMAP" title=" UMAP"> UMAP</a>, <a href="https://publications.waset.org/abstracts/search?q=fashion%20MNIST" title=" fashion MNIST"> fashion MNIST</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/137765/a-t-sne-and-umap-based-neural-network-image-classification-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137765.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">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8036</span> Margin-Based Feed-Forward Neural Network Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaohan%20Bookman">Xiaohan Bookman</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoyan%20Zhu"> Xiaoyan Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Max-Margin%20Principle" title="Max-Margin Principle">Max-Margin Principle</a>, <a href="https://publications.waset.org/abstracts/search?q=Feed-Forward%20Neural%20Network" title=" Feed-Forward Neural Network"> Feed-Forward Neural Network</a>, <a href="https://publications.waset.org/abstracts/search?q=classifier" title=" classifier"> classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20risk" title=" structural risk"> structural risk</a> </p> <a href="https://publications.waset.org/abstracts/27178/margin-based-feed-forward-neural-network-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27178.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">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8035</span> Recognition of Tifinagh Characters with Missing Parts Using Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El%20Mahdi%20Barrah">El Mahdi Barrah</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Safi"> Said Safi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdessamad%20Malaoui"> Abdessamad Malaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tifinagh%20character%20recognition" title="Tifinagh character recognition">Tifinagh character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20cost%20computation" title=" local cost computation"> local cost computation</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a> </p> <a href="https://publications.waset.org/abstracts/45178/recognition-of-tifinagh-characters-with-missing-parts-using-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45178.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">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8034</span> Artificial Neural Network Speed Controller for Excited DC Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elabed%20Saud">Elabed Saud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANNs%29" title="Artificial Neural Network (ANNs)">Artificial Neural Network (ANNs)</a>, <a href="https://publications.waset.org/abstracts/search?q=excited%20DC%20motor" title=" excited DC motor"> excited DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=convenional%20controller" title=" convenional controller"> convenional controller</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20Controller" title=" speed Controller"> speed Controller</a> </p> <a href="https://publications.waset.org/abstracts/21941/artificial-neural-network-speed-controller-for-excited-dc-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21941.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">726</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8033</span> An IM-COH Algorithm Neural Network Optimization with Cuckoo Search Algorithm for Time Series Samples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wullapa%20Wongsinlatam">Wullapa Wongsinlatam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20algorithm" title=" back propagation algorithm"> back propagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20minima%20problem" title=" local minima problem"> local minima problem</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20optimization" title=" metaheuristic optimization"> metaheuristic optimization</a> </p> <a href="https://publications.waset.org/abstracts/100995/an-im-coh-algorithm-neural-network-optimization-with-cuckoo-search-algorithm-for-time-series-samples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100995.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">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8032</span> Optimization of Structures Subjected to Earthquake</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lavaei">Alireza Lavaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lohrasbi"> Alireza Lohrasbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadali%20M.%20Shahlaei"> Mohammadali M. Shahlaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a> </p> <a href="https://publications.waset.org/abstracts/53234/optimization-of-structures-subjected-to-earthquake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53234.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">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8031</span> Modelling Fluoride Pollution of Groundwater Using Artificial Neural Network in the Western Parts of Jharkhand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neeta%20Kumari">Neeta Kumari</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopal%20Pathak"> Gopal Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20neural%20network%20%28ANN%29" title="Artificial neural network (ANN)">Artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=FFN%20%28Feed-forward%20network%29" title=" FFN (Feed-forward network)"> FFN (Feed-forward network)</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation%20algorithm" title=" backpropagation algorithm"> backpropagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Levenberg-Marquardt%20algorithm" title=" Levenberg-Marquardt algorithm"> Levenberg-Marquardt algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20fluoride%20contamination" title=" groundwater fluoride contamination"> groundwater fluoride contamination</a> </p> <a href="https://publications.waset.org/abstracts/19324/modelling-fluoride-pollution-of-groundwater-using-artificial-neural-network-in-the-western-parts-of-jharkhand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19324.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">550</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8030</span> Classification of Myoelectric Signals Using Multilayer Perceptron Neural Network with Back-Propagation Algorithm in a Wireless Surface Myoelectric Prosthesis of the Upper-Limb</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20D.%20Manalo">Kevin D. Manalo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jumelyn%20L.%20Torres"> Jumelyn L. Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Noel%20B.%20Linsangan"> Noel B. Linsangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title="field programmable gate array">field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20network" title=" multilayer perceptron neural network"> multilayer perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=verilog" title=" verilog"> verilog</a>, <a href="https://publications.waset.org/abstracts/search?q=zigbee" title=" zigbee"> zigbee</a> </p> <a href="https://publications.waset.org/abstracts/19846/classification-of-myoelectric-signals-using-multilayer-perceptron-neural-network-with-back-propagation-algorithm-in-a-wireless-surface-myoelectric-prosthesis-of-the-upper-limb" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19846.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">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8029</span> A Hybrid Hopfield Neural Network for Dynamic Flexible Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new hybrid Hopfield neural network is proposed for the dynamic, flexible job shop scheduling problem. A new heuristic based and easy to implement energy function is designed for the Hopfield neural network, which penalizes the constraints violation and decreases makespan. Moreover, for enhancing the performance, several heuristics are integrated to it that achieve active, and non-delay schedules also, prevent early convergence of the neural network. The suggested algorithm that is designed as a generalization of the previous studies for the flexible and dynamic scheduling problems can be used for solving real scheduling problems. Comparison of the presented hybrid method results with the previous studies results proves its efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20flexible%20job%20shop%20scheduling" title="dynamic flexible job shop scheduling">dynamic flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=constrained%20optimization" title=" constrained optimization"> constrained optimization</a> </p> <a href="https://publications.waset.org/abstracts/72143/a-hybrid-hopfield-neural-network-for-dynamic-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72143.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">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8028</span> Pion/Muon Identification in a Nuclear Emulsion Cloud Chamber Using Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kais%20Manai">Kais Manai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nuclear%20emulsion" title="nuclear emulsion">nuclear emulsion</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20identification" title=" particle identification"> particle identification</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/1437/pionmuon-identification-in-a-nuclear-emulsion-cloud-chamber-using-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1437.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">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8027</span> A Hybrid Feature Selection Algorithm with Neural Network for Software Fault Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalaf%20Khatatneh">Khalaf Khatatneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabeel%20Al-Milli"> Nabeel Al-Milli</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Hudaib"> Amjad Hudaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Monther%20Ali%20Tarawneh"> Monther Ali Tarawneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20fault%20prediction" title=" software fault prediction"> software fault prediction</a> </p> <a href="https://publications.waset.org/abstracts/167733/a-hybrid-feature-selection-algorithm-with-neural-network-for-software-fault-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167733.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">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8026</span> Orthogonal Basis Extreme Learning Algorithm and Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Li">Ying Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Li"> Yan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20basis%20extreme%20learning" title=" orthogonal basis extreme learning"> orthogonal basis extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a> </p> <a href="https://publications.waset.org/abstracts/15129/orthogonal-basis-extreme-learning-algorithm-and-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15129.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">534</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8025</span> A Video Surveillance System Using an Ensemble of Simple Neural Network Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20S.%20Moreira">Rodrigo S. Moreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson%20F.%20F.%20Ebecken"> Nelson F. F. Ebecken</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a maritime vessel tracker composed of an ensemble of WiSARD weightless neural network classifiers. A failure detector analyzes vessel movement with a Kalman filter and corrects the tracking, if necessary, using FFT matching. The use of the WiSARD neural network to track objects is uncommon. The additional contributions of the present study include a performance comparison with four state-of-art trackers, an experimental study of the features that improve maritime vessel tracking, the first use of an ensemble of classifiers to track maritime vessels and a new quantization algorithm that compares the values of pixel pairs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ram%20memory" title="ram memory">ram memory</a>, <a href="https://publications.waset.org/abstracts/search?q=WiSARD%20weightless%20neural%20network" title=" WiSARD weightless neural network"> WiSARD weightless neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/49928/a-video-surveillance-system-using-an-ensemble-of-simple-neural-network-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49928.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">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8024</span> Medical Neural Classifier Based on Improved Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fadzil%20Ahmad">Fadzil Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Noor%20Ashidi%20Mat%20Isa"> Noor Ashidi Mat Isa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20clasification" title=" pattern clasification"> pattern clasification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20accuracy" title=" classification accuracy"> classification accuracy</a> </p> <a href="https://publications.waset.org/abstracts/14231/medical-neural-classifier-based-on-improved-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14231.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">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8023</span> Methaheuristic Bat Algorithm in Training of Feed-Forward Neural Network for Stock Price Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marjan%20Golmaryami">Marjan Golmaryami</a>, <a href="https://publications.waset.org/abstracts/search?q=Marzieh%20Behzadi"> Marzieh Behzadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title="artificial neural network (ANN)">artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=bat%20algorithm" title=" bat algorithm"> bat algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization%20algorithm%20%28PSO%29" title=" particle swarm optimization algorithm (PSO)"> particle swarm optimization algorithm (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20exchange" title=" stock exchange"> stock exchange</a> </p> <a href="https://publications.waset.org/abstracts/14574/methaheuristic-bat-algorithm-in-training-of-feed-forward-neural-network-for-stock-price-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14574.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">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8022</span> Optimization of Vertical Axis Wind Turbine Based on Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Affanuddin%20H.%20Siddique">Mohammed Affanuddin H. Siddique</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayesh%20S.%20Shukla"> Jayesh S. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Chetan%20B.%20Meshram"> Chetan B. Meshram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VAWT" title="VAWT">VAWT</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20design" title=" inverse design"> inverse design</a> </p> <a href="https://publications.waset.org/abstracts/91997/optimization-of-vertical-axis-wind-turbine-based-on-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91997.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">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8021</span> Optimal Cropping Pattern in an Irrigation Project: A Hybrid Model of Artificial Neural Network and Modified Simplex Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safayat%20Ali%20Shaikh">Safayat Ali Shaikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20irrigation%20project" title=" large irrigation project"> large irrigation project</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20simplex%20algorithm" title=" modified simplex algorithm"> modified simplex algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20cropping%20pattern" title=" optimal cropping pattern"> optimal cropping pattern</a> </p> <a href="https://publications.waset.org/abstracts/71956/optimal-cropping-pattern-in-an-irrigation-project-a-hybrid-model-of-artificial-neural-network-and-modified-simplex-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71956.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">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8020</span> Solving the Quadratic Programming Problem Using a Recurrent Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Behroozpoor">A. A. Behroozpoor</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Mazarei"> M. M. Mazarei </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=REFERENCES%20%20%0D%0A%5B1%5D%09Xia" title="REFERENCES [1] Xia">REFERENCES [1] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y" title=" Y"> Y</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20new%20neural%20network%20for%20solving%20linear%20and%20quadratic%20programming%20problems.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks"> A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=7%286%29" title=" 7(6)"> 7(6)</a>, <a href="https://publications.waset.org/abstracts/search?q=1996" title=" 1996"> 1996</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.1544%E2%80%931548.%0D%0A%5B2%5D%09Xia" title=" pp.1544–1548. [2] Xia"> pp.1544–1548. [2] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20Wang" title=" & Wang"> & Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=J" title=" J"> J</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20recurrent%20neural%20network%20for%20solving%20nonlinear%20convex%20programs%20subject%20to%20linear%20constraints.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks"> A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=16%282%29" title="16(2)">16(2)</a>, <a href="https://publications.waset.org/abstracts/search?q=2005" title=" 2005"> 2005</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%20379%E2%80%93386.%0D%0A%5B3%5D%09Xia" title=" pp. 379–386. [3] Xia"> pp. 379–386. [3] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=H" title=" H"> H</a>, <a href="https://publications.waset.org/abstracts/search?q=Leung" title=" Leung"> Leung</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20J" title=" & J"> & J</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang" title=" Wang"> Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20projection%20neural%20network%20and%20its%20application%20to%20constrained%20optimization%20problems.%20IEEE%20Transactions%20Circuits%20and%20Systems-I" title=" A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I"> A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I</a>, <a href="https://publications.waset.org/abstracts/search?q=49%284%29" title=" 49(4)"> 49(4)</a>, <a href="https://publications.waset.org/abstracts/search?q=2002" title=" 2002"> 2002</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.447%E2%80%93458.B.%20%0D%0A%5B4%5D%09Q.%20Liu" title=" pp.447–458.B. [4] Q. Liu"> pp.447–458.B. [4] Q. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Guo" title=" Z. Guo"> Z. Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Wang" title=" J. Wang"> J. Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20one-layer%20recurrent%20neural%20network%20for%20constrained%20seudoconvex%20optimization%20and%20its%20application%20for%20dynamic%20portfolio%20optimization.%20Neural%20Networks" title=" A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks"> A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=26" title=" 26"> 26</a>, <a href="https://publications.waset.org/abstracts/search?q=2012" title=" 2012"> 2012</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%2099-109." title=" pp. 99-109. "> pp. 99-109. </a> </p> <a href="https://publications.waset.org/abstracts/19435/solving-the-quadratic-programming-problem-using-a-recurrent-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19435.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">643</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8019</span> Particle Filter Supported with the Neural Network for Aircraft Tracking Based on Kernel and Active Contour</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Izadkhah">Mohammad Izadkhah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Hoseini"> Mojtaba Hoseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Khalili%20Tehrani"> Alireza Khalili Tehrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20tracking" title="video tracking">video tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20snake" title=" greedy snake"> greedy snake</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/11913/particle-filter-supported-with-the-neural-network-for-aircraft-tracking-based-on-kernel-and-active-contour" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11913.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">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8018</span> Classification of Echo Signals Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisulu%20Tileukulova">Aisulu Tileukulova</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhexebay%20Dauren"> Zhexebay Dauren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radar" title="radar">radar</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=echo%20signals" title=" echo signals"> echo signals</a> </p> <a href="https://publications.waset.org/abstracts/147596/classification-of-echo-signals-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147596.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">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8017</span> Employing Bayesian Artificial Neural Network for Evaluation of Cold Rolling Force</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Kooche%20Baghy">P. Kooche Baghy</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Eskandari"> S. Eskandari</a>, <a href="https://publications.waset.org/abstracts/search?q=E.javanmard"> E.javanmard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=cold%20rolling" title=" cold rolling"> cold rolling</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20evaluation" title=" force evaluation"> force evaluation</a> </p> <a href="https://publications.waset.org/abstracts/47601/employing-bayesian-artificial-neural-network-for-evaluation-of-cold-rolling-force" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47601.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">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8016</span> Hyperspectral Data Classification Algorithm Based on the Deep Belief and Self-Organizing Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Qingjian">Li Qingjian</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Ke"> Li Ke</a>, <a href="https://publications.waset.org/abstracts/search?q=He%20Chun"> He Chun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Yong"> Huang Yong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBN" title="DBN">DBN</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20compression" title=" data compression"> data compression</a> </p> <a href="https://publications.waset.org/abstracts/89759/hyperspectral-data-classification-algorithm-based-on-the-deep-belief-and-self-organizing-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89759.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">341</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20network%20algorithm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20network%20algorithm&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20network%20algorithm&page=4">4</a></li> <li class="page-item"><a class="page-link" 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