Sinossi
This book focuses on the real-coded genetic algorithm and different topologies of feed-forward neural networks. Results in the following areas will be reported: (1) a real-coded genetic algorithm with new crossover and mutation operations, and its applications; (2) three different topologies of variable feed-forward neural networks, and their applications to short-term electric load forecasting and hand-written graffiti recognition. The real-coded genetic algorithm (RCGA) is one evolutionary computation technique that can tackle complex optimization problems. In this book, RCGA with new genetic operations called the average-bound crossover (ABX) and wavelet mutation (WM) will be presented. The three proposed topologies of variable feed- forward network networks are: (1) the variable- structure neural network (VSNN), (2) the variable- parameter neural network (VPNN), and (3) the variable-node-to-node-link neural network (VN2NN). By taking advantage of these networks' structures, the learning and generalization abilities of the networks can be increased. All the network parameters are tuned by the RCGA with ABX and WM.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
L'autore
Steve Ling received the Ph.D. degrees in 2006. Currently, he works in University of Technology, Sydney, Australia as Chancellor’s Postdoctoral Research Fellow (Lecturer). He has authored and coauthored over 70 international journal and conference papers on Computational Intelligence and its industrial applications.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Genetic Algorithm and Variable Feed-Forward Neural Networks
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LAP LAMBERT Academic Publishing Okt 2010, 2010
ISBN 10: 3843367299
ISBN 13: 9783843367295
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on the real-coded genetic algorithm and different topologies of feed-forward neural networks. Results in the following areas will be reported: (1) a real-coded genetic algorithm with new crossover and mutation operations, and its applications; (2) three different topologies of variable feed-forward neural networks, and their applications to short-term electric load forecasting and hand-written graffiti recognition. The real-coded genetic algorithm (RCGA) is one evolutionary computation technique that can tackle complex optimization problems. In this book, RCGA with new genetic operations called the average-bound crossover (ABX) and wavelet mutation (WM) will be presented. The three proposed topologies of variable feed- forward network networks are: (1) the variable- structure neural network (VSNN), (2) the variable- parameter neural network (VPNN), and (3) the variable-node-to-node-link neural network (VN2NN). By taking advantage of these networks' structures, the learning and generalization abilities of the networks can be increased. All the network parameters are tuned by the RCGA with ABX and WM. 252 pp. Englisch. Codice articolo 9783843367295
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Genetic Algorithm and Variable Feed-Forward Neural Networks
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LAP LAMBERT Academic Publishing Okt 2010, 2010
ISBN 10: 3843367299
ISBN 13: 9783843367295
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book focuses on the real-coded genetic algorithm and different topologies of feed-forward neural networks. Results in the following areas will be reported: (1) a real-coded genetic algorithm with new crossover and mutation operations, and its applications; (2) three different topologies of variable feed-forward neural networks, and their applications to short-term electric load forecasting and hand-written graffiti recognition. The real-coded genetic algorithm (RCGA) is one evolutionary computation technique that can tackle complex optimization problems. In this book, RCGA with new genetic operations called the average-bound crossover (ABX) and wavelet mutation (WM) will be presented. The three proposed topologies of variable feed- forward network networks are: (1) the variable- structure neural network (VSNN), (2) the variable- parameter neural network (VPNN), and (3) the variable-node-to-node-link neural network (VN2NN). By taking advantage of these networks' structures, the learning and generalization abilities of the networks can be increased. All the network parameters are tuned by the RCGA with ABX and WM.Books on Demand GmbH, Überseering 33, 22297 Hamburg 252 pp. Englisch. Codice articolo 9783843367295
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Genetic Algorithm and Variable Feed-Forward Neural Networks : Theory and application
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LAP LAMBERT Academic Publishing, 2010
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book focuses on the real-coded genetic algorithm and different topologies of feed-forward neural networks. Results in the following areas will be reported: (1) a real-coded genetic algorithm with new crossover and mutation operations, and its applications; (2) three different topologies of variable feed-forward neural networks, and their applications to short-term electric load forecasting and hand-written graffiti recognition. The real-coded genetic algorithm (RCGA) is one evolutionary computation technique that can tackle complex optimization problems. In this book, RCGA with new genetic operations called the average-bound crossover (ABX) and wavelet mutation (WM) will be presented. The three proposed topologies of variable feed- forward network networks are: (1) the variable- structure neural network (VSNN), (2) the variable- parameter neural network (VPNN), and (3) the variable-node-to-node-link neural network (VN2NN). By taking advantage of these networks' structures, the learning and generalization abilities of the networks can be increased. All the network parameters are tuned by the RCGA with ABX and WM. Codice articolo 9783843367295
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Genetic Algorithm and Variable Feed-Forward Neural Networks: Theory and application
Editore:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843367299
ISBN 13: 9783843367295
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