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首页Neural Network Design - 2nd Edition
Since this is a book on the design of neural networks, our choice of topics was guided by two principles. First, we wanted to present the most useful and practical neural network architectures, learning rules and training techniques. Second, we wanted the book to be complete in itself and to flow easily from one chapter to the next. For this reason, various introductory materials and chapters on applied mathematics are included just before they are needed for a particular subject. In summary, we have chosen some topics because of their practical importance in the application of neural networks, and other topics because of their importance in explaining how neural networks operate.
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Neural
Network
Design
2nd Edition
Hagan
Demuth
Beale
De Jesús

Neural Network Design
2nd Edtion
Martin T. Hagan
Oklahoma State University
Stillwater, Oklahoma
Howard B. Demuth
University of Colorado
Boulder, Colorado
Mark Hudson Beale
MHB Inc.
Hayden, Idaho
Orlando De Jesús
Consultant
Frisco, Texas

Copyright by Martin T. Hagan and Howard B. Demuth. All rights reserved. No part of the book
may be reproduced, stored in a retrieval system, or transcribed in any form or by any means -
electronic, mechanical, photocopying, recording or otherwise - without the prior permission of
Hagan and Demuth.
MTH
To Janet, Thomas, Daniel, Mom and Dad
HBD
To Hal, Katherine, Kimberly and Mary
MHB
To Leah, Valerie, Asia, Drake, Coral and Morgan
ODJ
To: Marisela, María Victoria
, Manuel, Mamá y Papá.
Neural Network Design, 2nd Edition, eBook
OVERHEADS and DEMONSTRATION PROGRAMS can be found at the following website:
hagan.okstate.edu/nnd.html
A somewhat condensed paperback version of this tex
t can be ordered from Amazon.

i
Contents
Preface
Introduction
Objectives 1-1
History 1-2
Applications 1-5
Biological Inspiration 1-8
Further Reading 1-10
Neuron Model and Network Architectures
Objectives 2-1
Theory and Examples 2-2
Notation 2-2
Neuron Model 2-2
Single-Input Neuron 2-2
Transfer Functions 2-3
Multiple-Input Neuron 2-7
Network Architectures 2-9
A Layer of Neurons 2-9
Multiple Layers of Neurons 2-10
Recurrent Networks 2-13
Summary of Results 2-16
Solved Problems 2-20
Epilogue 2-22
Exercises 2-23
2

ii
An Illustrative Example
Objectives 3-1
Theory and Examples 3-2
Problem Statement 3-2
Perceptron 3-3
Two-Input Case 3-4
Pattern Recognition Example 3-5
Hamming Network 3-8
Feedforward Layer 3-8
Recurrent Layer 3-9
Hopfield Network 3-12
Epilogue 3-15
Exercises 3-16
Perceptron Learning Rule
Objectives 4-1
Theory and Examples 4-2
Learning Rules 4-2
Perceptron Architecture 4-3
Single-Neuron Perceptron 4-5
Multiple-Neuron Perceptron 4-8
Perceptron Learning Rule 4-8
Test Problem 4-9
Constructing Learning Rules 4-10
Unified Learning Rule 4-12
Training Multiple-Neuron Perceptrons 4-13
Proof of Convergence 4-15
Notation 4-15
Proof 4-16
Limitations 4-18
Summary of Results 4-20
Solved Problems 4-21
Epilogue 4-33
Further Reading 4-34
Exercises 4-36
3
4
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