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Neural Network Design
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  • Title: Neural Network Design
  • Author(s) Martin T. Hagan
  • Publisher: Martin Hagan; 2 edition (September 1, 2014)
  • Paperback: 800 pages
  • eBook: PDF (1012 pages, 11 MB)
  • Language: English
  • ISBN-10: 0971732116
  • ISBN-13: 978-0971732117
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Book Description

This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules.

In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks.

In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks.

A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems.

About the Authors
  • Martin T. Hagan (Ph.D. Electrical Engineering, University of Kansas) has taught and conducted research in the areas of control systems and signal processing for the last 35 years. For the last 25 years his research has focused on the use of neural networks for control, filtering and prediction. He is a Professor in the School of Electrical and Computer Engineering at Oklahoma State University and a co-author of the Neural Network Toolbox for MATLAB.
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