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Clustering and Neural Network Approaches for General NN-Simulator: K-means, K-mediods, Recurrent Backpropagation,and Artificial Neural Network Simulator - Softcover
Synopsis
This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator).
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About the Author
Chandan Srivastava is doctoral research fellow at Departament de Enginyeria Química, Universitat Rovira i Virgili, Tarragona, Spain. He did his master research project at the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India. His area of research includes Bioinformatics and computational environmental engineering.
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Clustering and Neural Network Approaches for General NN-Simulator
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LAP LAMBERT Academic Publishing Jul 2011, 2011
ISBN 10: 3845409428
ISBN 13: 9783845409429
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator). 68 pp. Englisch. Seller Inventory # 9783845409429
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Clustering and Neural Network Approaches for General NN-Simulator
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ISBN 13: 9783845409429
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Clustering and Neural Network Approaches for General NN-Simulator : K-means, K-mediods, Recurrent Backpropagation,and Artificial Neural Network Simulator
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845409428
ISBN 13: 9783845409429
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator). Seller Inventory # 9783845409429
Seller Image
Clustering and Neural Network Approaches for General NN-Simulator
Published by
LAP LAMBERT Academic Publishing Jul 2011, 2011
ISBN 10: 3845409428
ISBN 13: 9783845409429
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Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch. Seller Inventory # 9783845409429
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Clustering and Neural Network Approaches for General NN-Simulator | K-means, K-mediods, Recurrent Backpropagation,and Artificial Neural Network Simulator
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ISBN 10: 3845409428
ISBN 13: 9783845409429
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Taschenbuch. Condition: Neu. Clustering and Neural Network Approaches for General NN-Simulator | K-means, K-mediods, Recurrent Backpropagation,and Artificial Neural Network Simulator | Chandan Srivastava | Taschenbuch | 68 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845409429 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106906332