Genetic Algorithm and Variable Feed-Forward Neural Networks | Theory and application

Steve Ling

ISBN 10: 3843367299 / ISBN 13: 9783843367295

Published by LAP LAMBERT Academic Publishing, 2010

About this Item

Genetic Algorithm and Variable Feed-Forward Neural Networks | Theory and application | Steve Ling | Taschenbuch | 252 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843367295 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 107243768

Bibliographic Details

• Title: Genetic Algorithm and Variable Feed-Forward Neural Networks | Theory and application
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2010
• Language: English
• ISBN 10: 3843367299
• ISBN 13: 9783843367295
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 16D millimeters 220 millimeters width by 150 millimeters height by 16 millimeters depth
• Weight: 393 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.