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feed-forward</title> <meta name="description" content="Search results for: feed-forward"> <meta name="keywords" content="feed-forward"> <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 navbar-toggler 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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="feed-forward"> <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> 32</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: feed-forward</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">32</span> Model-Based Control for Piezoelectric-Actuated Systems Using Inverse Prandtl-Ishlinskii Model and Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jin-Wei%20Liang">Jin-Wei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hung-Yi%20Chen"> Hung-Yi Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lung%20Lin"> Lung Lin </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper feedforward controller is designed to eliminate nonlinear hysteresis behaviors of a piezoelectric stack actuator (PSA) driven system. The control design is based on inverse Prandtl-Ishlinskii (P-I) hysteresis model identified using particle swarm optimization (PSO) technique. Based on the identified P-I model, both the inverse P-I hysteresis model and feedforward controller can be determined. Experimental results obtained using the inverse P-I feedforward control are compared with their counterparts using hysteresis estimates obtained from the identified Bouc-Wen model. Effectiveness of the proposed feedforward control scheme is demonstrated. To improve control performance feedback compensation using traditional PID scheme is adopted to integrate with the feedforward controller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20Bouc-Wen%20hysteresis%20model" title="the Bouc-Wen hysteresis model">the Bouc-Wen hysteresis model</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=Prandtl-Ishlinskii%20model" title=" Prandtl-Ishlinskii model"> Prandtl-Ishlinskii model</a>, <a href="https://publications.waset.org/abstracts/search?q=automation%20engineering" title=" automation engineering"> automation engineering</a> </p> <a href="https://publications.waset.org/abstracts/4325/model-based-control-for-piezoelectric-actuated-systems-using-inverse-prandtl-ishlinskii-model-and-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4325.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">514</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">31</span> Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natalia%20%20Espinosa">Natalia Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Amorim"> Arthur Amorim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudolf%20%20Huebner"> Rudolf Huebner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG. <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=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Epilepsy%20Detection" title=" Epilepsy Detection "> Epilepsy Detection </a>, <a href="https://publications.waset.org/abstracts/search?q=Seizure." title=" Seizure."> Seizure.</a> </p> <a href="https://publications.waset.org/abstracts/122872/feedforward-neural-network-with-backpropagation-for-epilepsy-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122872.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">223</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">30</span> Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Payal">Ashish Payal</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Rai"> C. S. Rai</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20V.%20R.%20Reddy"> B. V. R. Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m<sup>2</sup> grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=localization" title="localization">localization</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</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=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=RSSI" title=" RSSI"> RSSI</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a> </p> <a href="https://publications.waset.org/abstracts/49637/comparative-analysis-of-sigmoidal-feedforward-artificial-neural-networks-and-radial-basis-function-networks-approach-for-localization-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49637.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">339</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">29</span> Artificial Intelligence in the Design of High-Strength Recycled Concrete</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadi%20Rouhi%20Belvirdi">Hadi Rouhi Belvirdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Davoud%20Beheshtizadeh"> Davoud Beheshtizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing demand for sustainable construction materials has led to a growing interest in high-strength recycled concrete (HSRC). Utilizing recycled materials not only reduces waste but also minimizes the depletion of natural resources. This study explores the application of artificial intelligence (AI) techniques to model and predict the properties of HSRC. In the past two decades, the production levels in various industries and, consequently, the amount of waste have increased significantly. Continuing this trend will undoubtedly cause irreparable damage to the environment. For this reason, engineers have been constantly seeking practical solutions for recycling industrial waste in recent years. This research utilized the results of the compressive strength of 90-day high-strength recycled concrete. The method for creating recycled concrete involved replacing sand with crushed glass and using glass powder instead of cement. Subsequently, a feedforward artificial neural network was employed to model the compressive strength results for 90 days. The regression and error values obtained indicate that this network is suitable for modeling the compressive strength data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-strength%20recycled%20concrete" title="high-strength recycled concrete">high-strength recycled concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20artificial%20neural%20network" title=" feedforward artificial neural network"> feedforward artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20materials" title=" construction materials"> construction materials</a> </p> <a href="https://publications.waset.org/abstracts/193212/artificial-intelligence-in-the-design-of-high-strength-recycled-concrete" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193212.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">13</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">28</span> Comparative Analysis of Control Techniques Based Sliding Mode for Transient Stability Assessment for Synchronous Multicellular Converter </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rihab%20Hamdi">Rihab Hamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Hadri%20Hamida"> Amel Hadri Hamida</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatiha%20Khelili"> Fatiha Khelili</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakina%20Zerouali"> Sakina Zerouali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ouafae%20Bennis"> Ouafae Bennis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper features a comparative study performance of sliding mode controller (SMC) for closed-loop voltage control of direct current to direct current (DC-DC) three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM) with SMC based on hysteresis modulation (HM) where an adaptive feedforward technique is adopted. On one hand, for the PWM-based SM, the approach is to incorporate a fixed-frequency PWM scheme which is eﬀectively a variant of SM control. On the other hand, for the HM-based SM, oncoming an adaptive feedforward control that makes the hysteresis band variable in the hysteresis modulator of the SM controller in the aim to restrict the switching frequency variation in the case of any change of the line input voltage or output load variation are introduced. The results obtained under load change, input change and reference change clearly demonstrates a similar dynamic response of both proposed techniques, their eﬀectiveness is fast and smooth tracking of the desired output voltage. The PWM-based SM technique has greatly improved the dynamic behavior with a bit advantageous compared to the HM-based SM technique, as well as provide stability in any operating conditions. Simulation studies in MATLAB/Simulink environment have been performed to verify the concept. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DC-DC%20converter" title="DC-DC converter">DC-DC converter</a>, <a href="https://publications.waset.org/abstracts/search?q=hysteresis%20modulation" title=" hysteresis modulation"> hysteresis modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20multi-cells%20converter" title=" parallel multi-cells converter"> parallel multi-cells converter</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse-width%20modulation" title=" pulse-width modulation"> pulse-width modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title=" sliding mode control"> sliding mode control</a> </p> <a href="https://publications.waset.org/abstracts/116357/comparative-analysis-of-control-techniques-based-sliding-mode-for-transient-stability-assessment-for-synchronous-multicellular-converter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116357.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">167</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">27</span> Decision Support System for Fetus Status Evaluation Using Cardiotocograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oyebade%20K.%20Oyedotun">Oyebade K. Oyedotun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support" title="decision support">decision support</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiotocogram" title=" cardiotocogram"> cardiotocogram</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</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/32333/decision-support-system-for-fetus-status-evaluation-using-cardiotocograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32333.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">332</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">26</span> Artificial Neural Network Based Approach in Prediction of Potential Water Pollution Across Different Land-Use Patterns </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.R%C3%BC%C5%9Ft%C3%BC%20Karaman">M.Rüştü Karaman</a>, <a href="https://publications.waset.org/abstracts/search?q=%C4%B0smail%20%C4%B0%C5%9Feri"> İsmail İşeri</a>, <a href="https://publications.waset.org/abstracts/search?q=Kadir%20Saltal%C4%B1"> Kadir Saltalı</a>, <a href="https://publications.waset.org/abstracts/search?q=A.Re%C5%9Fit%20Brohi"> A.Reşit Brohi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayhan%20Horuz"> Ayhan Horuz</a>, <a href="https://publications.waset.org/abstracts/search?q=M%C3%BCmin%20Dizman"> Mümin Dizman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=drainage%20water" title=" drainage water"> drainage water</a>, <a href="https://publications.waset.org/abstracts/search?q=nitrate%20pollution" title=" nitrate pollution"> nitrate pollution</a> </p> <a href="https://publications.waset.org/abstracts/11497/artificial-neural-network-based-approach-in-prediction-of-potential-water-pollution-across-different-land-use-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11497.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">25</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">24</span> Algorithms Inspired from Human Behavior Applied to Optimization of a Complex Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Curteanu">S. Curteanu</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Leon"> F. Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gavrilescu"> M. Gavrilescu</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Floria"> S. A. Floria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models. <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=human%20behaviors%20of%20learning%20and%20cooperation" title=" human behaviors of learning and cooperation"> human behaviors of learning and cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20competitive%20behavior" title=" human competitive behavior"> human competitive behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a> </p> <a href="https://publications.waset.org/abstracts/149011/algorithms-inspired-from-human-behavior-applied-to-optimization-of-a-complex-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149011.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">108</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">23</span> Assessment the Quality of Telecommunication Services by Fuzzy Inferences System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oktay%20Nusratov">Oktay Nusratov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Rzaev"> Ramin Rzaev</a>, <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Goyushov"> Aydin Goyushov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy inference method based approach to the forming of modular intellectual system of assessment the quality of communication services is proposed. Developed under this approach the basic fuzzy estimation model takes into account the recommendations of the International Telecommunication Union in respect of the operation of packet switching networks based on IP-protocol. To implement the main features and functions of the fuzzy control system of quality telecommunication services it is used multilayer feedforward neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20communication" title="quality of communication">quality of communication</a>, <a href="https://publications.waset.org/abstracts/search?q=IP-telephony" title=" IP-telephony"> IP-telephony</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20set" title=" fuzzy set"> fuzzy set</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20implication" title=" fuzzy implication"> fuzzy implication</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/15543/assessment-the-quality-of-telecommunication-services-by-fuzzy-inferences-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15543.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">468</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">22</span> Model Predictive Control of Turbocharged Diesel Engine with Exhaust Gas Recirculation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Yavas">U. Yavas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gokasan"> M. Gokasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title="predictive control">predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20control" title=" engine control"> engine control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20modeling" title=" engine modeling"> engine modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20control" title=" PID control"> PID control</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20compensation" title=" feedforward compensation"> feedforward compensation</a> </p> <a href="https://publications.waset.org/abstracts/34455/model-predictive-control-of-turbocharged-diesel-engine-with-exhaust-gas-recirculation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34455.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">636</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">21</span> Optimisation of the Input Layer Structure for Feedforward Narx Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zongyan%20Li">Zongyan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Matt%20Best"> Matt Best</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation%20analysis" title="correlation analysis">correlation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=F-ratio" title=" F-ratio"> F-ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=levenberg-marquardt" title=" levenberg-marquardt"> levenberg-marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=NARX" title=" NARX"> NARX</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=optimisation" title=" optimisation"> optimisation</a> </p> <a href="https://publications.waset.org/abstracts/23195/optimisation-of-the-input-layer-structure-for-feedforward-narx-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23195.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">372</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">20</span> Compact Low-Voltage Biomedical Instrumentation Amplifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phanumas%20Khumsat">Phanumas Khumsat</a>, <a href="https://publications.waset.org/abstracts/search?q=Chalermchai%20Janmane"> Chalermchai Janmane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low-voltage instrumentation amplifier has been proposed for 3-lead electrocardiogram measurement system. The circuit’s interference rejection technique is based upon common-mode feed-forwarding where common-mode currents have cancelled each other at the output nodes. The common-mode current for cancellation is generated by means of common-mode sensing and emitter or source followers with resistors employing only one transistor. Simultaneously this particular transistor also provides common-mode feedback to the patient’s right/left leg to further reduce interference entering the amplifier. The proposed designs have been verified with simulations in 0.18-µm CMOS process operating under 1.0-V supply with CMRR greater than 80dB. Moreover ECG signals have experimentally recorded with the proposed instrumentation amplifiers implemented from discrete BJT (BC547, BC558) and MOSFET (ALD1106, ALD1107) transistors working with 1.5-V supply. <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=common-mode%20feedback" title=" common-mode feedback"> common-mode feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=common-mode%20feedforward" title=" common-mode feedforward"> common-mode feedforward</a>, <a href="https://publications.waset.org/abstracts/search?q=communication%20engineering" title=" communication engineering"> communication engineering</a> </p> <a href="https://publications.waset.org/abstracts/4913/compact-low-voltage-biomedical-instrumentation-amplifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4913.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">384</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">19</span> Understanding and Improving Neural Network Weight Initialization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Aguirre">Diego Aguirre</a>, <a href="https://publications.waset.org/abstracts/search?q=Olac%20Fuentes"> Olac Fuentes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20initialization" title=" weight initialization"> weight initialization</a> </p> <a href="https://publications.waset.org/abstracts/89735/understanding-and-improving-neural-network-weight-initialization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89735.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">135</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">18</span> Nonparametric Sieve Estimation with Dependent Data: Application to Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chad%20Brown">Chad Brown</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sieve%20extremum%20estimates" title="sieve extremum estimates">sieve extremum estimates</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20estimation" title=" nonparametric estimation"> nonparametric estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</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=rectified%20linear%20unit" title=" rectified linear unit"> rectified linear unit</a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationary%20processes" title=" nonstationary processes"> nonstationary processes</a> </p> <a href="https://publications.waset.org/abstracts/186727/nonparametric-sieve-estimation-with-dependent-data-application-to-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186727.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">41</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">17</span> A Study on Green Building Certification Systems within the Context of Anticipatory Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taner%20Izzet%20Acarer">Taner Izzet Acarer</a>, <a href="https://publications.waset.org/abstracts/search?q=Ece%20Ceylan%20Baba"> Ece Ceylan Baba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines green building certification systems and their current processes in comparison with anticipatory systems. Rapid growth of human population and depletion of natural resources are causing irreparable damage to urban and natural environment. In this context, the concept of ‘sustainable architecture’ has emerged in the 20th century so as to establish and maintain standards for livable urban spaces, to improve quality of urban life, and to preserve natural resources for future generations. The construction industry is responsible for a large part of the resource consumption and it is believed that the ‘green building’ designs that emerge in construction industry can reduce environmental problems and contribute to sustainable development around the world. A building must meet a specific set of criteria, set forth through various certification systems, in order to be eligible for designation as a green building. It is disputable whether methods used by green building certification systems today truly serve the purposes of creating a sustainable world. Accordingly, this study will investigate the sets of rating systems used by the most popular green building certification programs, including LEED (Leadership in Energy and Environmental Design), BREEAM (Building Research Establishment's Environmental Assessment Methods), DGNB (Deutsche Gesellschaft für Nachhaltiges Bauen System), in terms of ‘Anticipatory Systems’ in accordance with the certification processes and their goals, while discussing their contribution to architecture. The basic methodology of the study is as follows. Firstly analyzes of brief historical and literature review of green buildings and certificate systems will be stated. Secondly, processes of green building certificate systems will be disputed by the help of anticipatory systems. Anticipatory Systems is a set of systems designed to generate action-oriented projections and to forecast potential side effects using the most current data. Anticipatory Systems pull the future into the present and take action based on future predictions. Although they do not have a claim to see into the future, they can provide foresight data. When shaping the foresight data, Anticipatory Systems use feedforward instead of feedback, enabling them to forecast the system’s behavior and potential side effects by establishing a correlation between the system’s present/past behavior and projected results. This study indicates the goals and current status of LEED, BREEAM and DGNB rating systems that created by using the feedback technique will be examined and presented in a chart. In addition, by examining these rating systems with the anticipatory system that using the feedforward method, the negative influences of the potential side effects on the purpose and current status of the rating systems will be shown in another chart. By comparing the two obtained data, the findings will be shown that rating systems are used for different goals than the purposes they are aiming for. In conclusion, the side effects of green building certification systems will be stated by using anticipatory system models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anticipatory%20systems" title="anticipatory systems">anticipatory systems</a>, <a href="https://publications.waset.org/abstracts/search?q=BREEAM" title=" BREEAM"> BREEAM</a>, <a href="https://publications.waset.org/abstracts/search?q=certificate%20systems" title=" certificate systems"> certificate systems</a>, <a href="https://publications.waset.org/abstracts/search?q=DGNB" title=" DGNB"> DGNB</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20buildings" title=" green buildings"> green buildings</a>, <a href="https://publications.waset.org/abstracts/search?q=LEED" title=" LEED"> LEED</a> </p> <a href="https://publications.waset.org/abstracts/93928/a-study-on-green-building-certification-systems-within-the-context-of-anticipatory-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93928.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">220</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">16</span> Predict Suspended Sediment Concentration Using Artificial Neural Networks Technique: Case Study Oued El Abiod Watershed, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Bougamouza">Adel Bougamouza</a>, <a href="https://publications.waset.org/abstracts/search?q=Boualam%20Remini"> Boualam Remini</a>, <a href="https://publications.waset.org/abstracts/search?q=Abd%20El%20Hadi%20Ammari"> Abd El Hadi Ammari</a>, <a href="https://publications.waset.org/abstracts/search?q=Feteh%20Sakhraoui"> Feteh Sakhraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision. <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=Oued%20Abiod%20watershed" title=" Oued Abiod watershed"> Oued Abiod watershed</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20network" title=" feedforward network"> feedforward network</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20regression%20network" title=" generalized regression network"> generalized regression network</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20network" title=" radial basis network"> radial basis network</a>, <a href="https://publications.waset.org/abstracts/search?q=sediment%20concentration" title=" sediment concentration"> sediment concentration</a> </p> <a href="https://publications.waset.org/abstracts/37324/predict-suspended-sediment-concentration-using-artificial-neural-networks-technique-case-study-oued-el-abiod-watershed-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37324.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">15</span> Prediction of Structural Response of Reinforced Concrete Buildings Using Artificial Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juan%20Boj%C3%B3rquez">Juan Bojórquez</a>, <a href="https://publications.waset.org/abstracts/search?q=Henry%20E.%20Reyes"> Henry E. Reyes</a>, <a href="https://publications.waset.org/abstracts/search?q=Ed%C3%A9n%20Boj%C3%B3rquez"> Edén Bojórquez</a>, <a href="https://publications.waset.org/abstracts/search?q=Alfredo%20Reyes-Salazar"> Alfredo Reyes-Salazar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20reliability" title="structural reliability">structural reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20design" title=" seismic design"> seismic design</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</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=probabilistic%20seismic%20hazard%20analysis" title=" probabilistic seismic hazard analysis"> probabilistic seismic hazard analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20demand%20hazard%20curves" title=" seismic demand hazard curves"> seismic demand hazard curves</a> </p> <a href="https://publications.waset.org/abstracts/141596/prediction-of-structural-response-of-reinforced-concrete-buildings-using-artificial-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141596.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">196</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">14</span> Simulation of Flow through Dam Foundation by FEM and ANN Methods Case Study: Shahid Abbaspour Dam</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Shahrbanozadeh">Mehrdad Shahrbanozadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Gholam%20Abbas%20Barani"> Gholam Abbas Barani</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Shojaee"> Saeed Shojaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=seepage" title="seepage">seepage</a>, <a href="https://publications.waset.org/abstracts/search?q=dam%20foundation" title=" dam foundation"> dam foundation</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</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=seep%203D%20model" title=" seep 3D model"> seep 3D model</a> </p> <a href="https://publications.waset.org/abstracts/20239/simulation-of-flow-through-dam-foundation-by-fem-and-ann-methods-case-study-shahid-abbaspour-dam" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20239.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">13</span> Modeling and Dynamics Analysis for Intelligent Skid-Steering Vehicle Based on Trucksim-Simulink</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yansong%20Zhang">Yansong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xueyuan%20Li"> Xueyuan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Junjie%20Zhou"> Junjie Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Xufeng%20Yin"> Xufeng Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Shihua%20Yuan"> Shihua Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuxian%20Liu"> Shuxian Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at the verification of control algorithms for skid-steering vehicles, a vehicle simulation model of 6×6 electric skid-steering unmanned vehicle was established based on Trucksim and Simulink. The original transmission and steering mechanism of Trucksim are removed, and the electric skid-steering model and a closed-loop controller for the vehicle speed and yaw rate are built in Simulink. The simulation results are compared with the ones got by theoretical formulas. The results show that the predicted tire mechanics and vehicle kinematics of Trucksim-Simulink simulation model are closed to the theoretical results. Therefore, it can be used as an effective approach to study the dynamic performance and control algorithm of skid-steering vehicle. In this paper, a method of motion control based on feed forward control is also designed. The simulation results show that the feed forward control strategy can make the vehicle follow the target yaw rate more quickly and accurately, which makes the vehicle have more maneuverability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=skid-steering" title="skid-steering">skid-steering</a>, <a href="https://publications.waset.org/abstracts/search?q=Trucksim-Simulink" title=" Trucksim-Simulink"> Trucksim-Simulink</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20control" title=" feedforward control"> feedforward control</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamics" title=" dynamics"> dynamics</a> </p> <a href="https://publications.waset.org/abstracts/84745/modeling-and-dynamics-analysis-for-intelligent-skid-steering-vehicle-based-on-trucksim-simulink" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84745.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">324</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">12</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">95</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">11</span> Identification System for Grading Banana in Food Processing Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebenezer%20O.%20Olaniyi">Ebenezer O. Olaniyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Oyebade%20K.%20Oyedotun"> Oyebade K. Oyedotun</a>, <a href="https://publications.waset.org/abstracts/search?q=Khashman%20Adnan"> Khashman Adnan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=banana" title="banana">banana</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20processing" title=" food processing"> food processing</a>, <a href="https://publications.waset.org/abstracts/search?q=identification%20system" title=" identification system"> identification system</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/31869/identification-system-for-grading-banana-in-food-processing-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31869.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">471</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">10</span> Data Mining of Students' Performance Using Artificial Neural Network: Turkish Students as a Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Nii%20Tackie">Samuel Nii Tackie</a>, <a href="https://publications.waset.org/abstracts/search?q=Oyebade%20K.%20Oyedotun"> Oyebade K. Oyedotun</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebenezer%20O.%20Olaniyi"> Ebenezer O. Olaniyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Khashman"> Adnan Khashman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time. <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=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=students%E2%80%99%20evaluation" title=" students’ evaluation"> students’ evaluation</a> </p> <a href="https://publications.waset.org/abstracts/25099/data-mining-of-students-performance-using-artificial-neural-network-turkish-students-as-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25099.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">613</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">9</span> Nelder-Mead Parametric Optimization of Elastic Metamaterials with Artificial Neural Network Surrogate Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiaqi%20Dong">Jiaqi Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing-Hua%20Qin"> Qing-Hua Qin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Xiao"> Yi Xiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%. <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20metamaterials" title=" mechanical metamaterials"> mechanical metamaterials</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelder-Mead%20optimization" title=" Nelder-Mead optimization"> Nelder-Mead optimization</a> </p> <a href="https://publications.waset.org/abstracts/110099/nelder-mead-parametric-optimization-of-elastic-metamaterials-with-artificial-neural-network-surrogate-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110099.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">128</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">8</span> Non-Linear Assessment of Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milica%20Karadzic">Milica Karadzic</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevric"> Lidija Jevric</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac-Kuzmanovic"> Sanja Podunavac-Kuzmanovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kovacevic"> Strahinja Kovacevic</a>, <a href="https://publications.waset.org/abstracts/search?q=Anamarija%20Mandic"> Anamarija Mandic</a>, <a href="https://publications.waset.org/abstracts/search?q=Katarina%20Penov%20Gasi"> Katarina Penov Gasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Marija%20Sakac"> Marija Sakac</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandar%20Okljesa"> Aleksandar Okljesa</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Nikolic"> Andrea Nikolic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology). <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=liquid%20chromatography" title=" liquid chromatography"> liquid chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=steroids" title=" steroids"> steroids</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20of%20ranking%20differences" title=" sum of ranking differences"> sum of ranking differences</a> </p> <a href="https://publications.waset.org/abstracts/50587/non-linear-assessment-of-chromatographic-lipophilicity-and-model-ranking-of-newly-synthesized-steroid-derivatives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50587.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">319</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">7</span> Setting Uncertainty Conditions Using Singular Values for Repetitive Control in State Feedback</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20A.%20Alsubaie">Muhammad A. Alsubaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Mubarak%20K.%20H.%20Alhajri"> Mubarak K. H. Alhajri</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20S.%20Altowaim"> Tarek S. Altowaim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A repetitive controller designed to accommodate periodic disturbances via state feedback is discussed. Periodic disturbances can be represented by a time delay model in a positive feedback loop acting on system output. A direct use of the small gain theorem solves the periodic disturbances problem via 1) isolating the delay model, 2) finding the overall system representation around the delay model and 3) designing a feedback controller that assures overall system stability and tracking error convergence. This paper addresses uncertainty conditions for the repetitive controller designed in state feedback in either past error feedforward or current error feedback using singular values. The uncertainty investigation is based on the overall system found and the stability condition associated with it; depending on the scheme used, to set an upper/lower limit weighting parameter. This creates a region that should not be exceeded in selecting the weighting parameter which in turns assures performance improvement against system uncertainty. Repetitive control problem can be described in lifted form. This allows the usage of singular values principle in setting the range for the weighting parameter selection. The Simulation results obtained show a tracking error convergence against dynamic system perturbation if the weighting parameter chosen is within the range obtained. Simulation results also show the advantage of weighting parameter usage compared to the case where it is omitted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20mismatch" title="model mismatch">model mismatch</a>, <a href="https://publications.waset.org/abstracts/search?q=repetitive%20control" title=" repetitive control"> repetitive control</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20values" title=" singular values"> singular values</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20feedback" title=" state feedback"> state feedback</a> </p> <a href="https://publications.waset.org/abstracts/99234/setting-uncertainty-conditions-using-singular-values-for-repetitive-control-in-state-feedback" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99234.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">155</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">6</span> Implications of Optimisation Algorithm on the Forecast Performance of Artificial Neural Network for Streamflow Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martins%20Y.%20Otache">Martins Y. Otache</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20J.%20Musa"> John J. Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=Abayomi%20I.%20Kuti"> Abayomi I. Kuti</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustapha%20Mohammed"> Mustapha Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=streamflow" title="streamflow">streamflow</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=optimisation" title=" optimisation"> optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a> </p> <a href="https://publications.waset.org/abstracts/132874/implications-of-optimisation-algorithm-on-the-forecast-performance-of-artificial-neural-network-for-streamflow-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132874.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">5</span> Derivatives Balance Method for Linear and Nonlinear Control Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Musaab%20Mohammed%20Ahmed%20Ali">Musaab Mohammed Ahmed Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Vodichev"> Vladimir Vodichev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> work deals with an universal control technique or single controller for linear and nonlinear stabilization and tracing control systems. These systems may be structured as SISO and MIMO. Parameters of controlled plants can vary over a wide range. Introduced a novel control systems design method, construction of stable platform orbits using derivative balance, solved transfer function stability preservation problem of linear system under partial substitution of a rational function. Universal controller is proposed as a polar system with the multiple orbits to simplify design procedure, where each orbit represent single order of controller transfer function. Designed controller consist of proportional, integral, derivative terms and multiple feedback and feedforward loops. The controller parameters synthesis method is presented. In generally, controller parameters depend on new polynomial equation where all parameters have a relationship with each other and have fixed values without requirements of retuning. The simulation results show that the proposed universal controller can stabilize infinity number of linear and nonlinear plants and shaping desired previously ordered performance. It has been proven that sensor errors and poor performance will be completely compensated and cannot affect system performance. Disturbances and noises effect on the controller loop will be fully rejected. Technical and economic effect of using proposed controller has been investigated and compared to adaptive, predictive, and robust controllers. The economic analysis shows the advantage of single controller with fixed parameters to drive infinity numbers of plants compared to above mentioned control techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=derivative%20balance" title="derivative balance">derivative balance</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20parameters" title=" fixed parameters"> fixed parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=stable%20platform" title=" stable platform"> stable platform</a>, <a href="https://publications.waset.org/abstracts/search?q=universal%20control" title=" universal control"> universal control</a> </p> <a href="https://publications.waset.org/abstracts/147745/derivatives-balance-method-for-linear-and-nonlinear-control-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147745.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">136</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">4</span> Comparison of Feedforward Back Propagation and Self-Organizing Map for Prediction of Crop Water Stress Index of Rice</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aschalew%20Cherie%20Workneh">Aschalew Cherie Workneh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Hari%20Prasad"> K. S. Hari Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Shekhar%20Prasad%20Ojha"> Chandra Shekhar Prasad Ojha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks%3B%20crop%20water%20stress%20index%3B%20canopy%20temperature" title="artificial neural networks; crop water stress index; canopy temperature">artificial neural networks; crop water stress index; canopy temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20capability" title=" prediction capability"> prediction capability</a> </p> <a href="https://publications.waset.org/abstracts/157887/comparison-of-feedforward-back-propagation-and-self-organizing-map-for-prediction-of-crop-water-stress-index-of-rice" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157887.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">117</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">3</span> A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks: Prediction of Influential Factors on Eating Behaviors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Kheirollahpour">Maryam Kheirollahpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Danaee"> Mahmoud Danaee</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Faisal%20Merican"> Amir Faisal Merican</a>, <a href="https://publications.waset.org/abstracts/search?q=Asma%20Ahmad%20Shariff"> Asma Ahmad Shariff</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20model" title="hybrid model">hybrid model</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modeling" title=" structural equation modeling"> structural equation modeling</a>, <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=eating%20behavior%20patterns" title=" eating behavior patterns"> eating behavior patterns</a> </p> <a href="https://publications.waset.org/abstracts/107892/a-hybrid-model-of-structural-equation-modelling-artificial-neural-networks-prediction-of-influential-factors-on-eating-behaviors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107892.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">156</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=feed-forward&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feed-forward&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> 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