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首页Neural Network Design (2nd Edition)
神经网络设计第二版pdf, Neural Network Design (2nd Edition)英语原版,2014年出版,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
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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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