Neural Network Design

by Martin T. Hagan, Howard B. Demuth, Mark H. Beale

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. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.

Genres: Computer Science, Artificial Intelligence

800 pages, Paperback

First published December 29, 1995

Reviews

[Ali Frz · Rating 4 out of 5]

کتابی بسیار حرفه ای، با نگارشی پیچیده و غامض

[Karan · Rating 5 out of 5]

Really clear. Best for a beginner. Very detailed.

[Arun]

Freely available here from the author's page - https://hagan.okstate.edu/NNDesign.pdf

[Darin]

This book provides a good introduction to neural networks and is well-written. There is a lot of math in it, but the authors have made the book about as readable as one could expect without simplifying things too much.

[Niran Pravithana · Rating 5 out of 5]

The simpliest yet powerful explanation of ANN available in the market. However advance level of algebra is still required.