Neural Network Design (2nd Edition)

【实例简介】
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
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. coi d Morgan ODJ To: Marisela, Maria victoria, Manuel, Mamd y papa 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 text can be ordered from Amazon Contents reface Introduction Objectives History 1-2 Applications Biological Inspiration Further Reading Neuron model and Network architectures h Objectives Theory and Examples 2-2 Notation 2-2 Neuron model sing」le- Input Neuron 2-2 Transfer functions 2-3 Multiple-Input Neuron Network Architectures 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 plogue 2-22 Exercises 2-23 An illustrative Example 3 z Objectives Theory and Examples 3-2 Problem statement 3-2 Perceptron 3-3 Two-Input case 3-4 Pattern Recognition example 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 Theory and EXamples 4-2 Learning Rules 4-2 Perceptron Architecture 4-3 Single-Neuron Perceptro 4-5 Multiple-Neuron Perceptron 4-8 Perceptron Learning Rule 4-8 Test proble 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 urther Reading 4-34 Exercises 4-36 Signal and Weight Vector Spaces 5 Objectives Theory and Examples 5-2 Linear Vector Spaces Linear Independence Spanning a Space 5-5 Inner product Norm 5-7 Orthogonality 5-7 Gram-Schmidt Orthogonalization 5-8 Vector Expansions Reciprocal Basis vectors 5-10 Summary of Results 5-14 Solved Problems 5-17 Epilogue 5-26 Further Reading 5-27 Exercises 5-28 6 Linear transformations for Neural networks Objectives Theory and Examples Linear Transformations 6-2 Matrix Representations Change of Basis 6-6 Eigenvalues and Eigenvectors 6-10 Diagonalization 6-13 Summary of Results 6-15 Solved problems 6-17 Epilogue 6-28 Further Reading 6-29 xercises 6-30 Supervised Hebbian Learning 7 Objectives 7-1 Theory and Examples 7-2 inear associato 7-3 The Hebb rule 7-4 Performance Analysis 7-5 Pseudoinverse rule Application 7-10 Variations of hebbian le 7-12 su ummary of results y 17-4 Solved Problems Epilogue 7-29 Further readi 7-30 Exercises 7-31 8 Performance Surfaces and Optimum Points Objectives Theory and Examples 8-2 Taylor Series 8-2 Vector case 8-4 Directional derivati Minima Necessary Conditions for Optimality First-Order conditions 8-10 Second-Order Conditions 8-11 Quadratic Functions 8-12 Eigensystem of the Hessian 8-13 Summary of results 8-20 Solved problems 8-22 plogue 8-34 Further Reading 8-35 Exercises 8-36 LL Performance Optimization 9 Objectives Theory and Examples 9-2 Steepest Descent 9-2 Stable Learning Rates 9-6 Minimizing along a Line 9-8 Newton's method 9-10 Conjugate Gradient 9-15 Summary of Results 9-21 Solved problems 9-23 Epilogue Further Reading 9-38 Exercises 9-39 10 Widrow-Hoff Learning Objectives 10-1 Theory and EXamples 102 ADALINE Network 10-2 Single ADALINE 10-3 Mean Square Error 10-4 LMS Algorithm 10-7 Analysis of Convergence 10-9 Adaptive Filtering 10-13 Adaptive Noise Cancellation 10-15 Echo Cancellation 10-21 Summary of Results 10-22 Solved Problems 10-24 Epilogue 10-40 Further Reading 10-41 Exercises 10-42 Backpropagation 11 Objectives 11-1 Theory and Examples Multilayer Perceptrons 11-2 Pattern Classification 11-3 Function Approximation 11-4 The Backpropagation algorithm 11-7 Performance Index 11-8 Chain Rule Backpropagating the Sensitivities 11-11 ary 11-13 Example 11-14 Batch vS Incremental Training 11-17 Using Backpropagation 11-18 Choice of Network architecture 11-18 Convergence 11-20 Generalization 1122 Summary of Results 11-25 Solved Problems 11-27 Epilogue 11-41 Further Reading 11-42 Exercises 11-44 12 Variations on Backpropagation Objectives 12-1 Theory and Examples 122 Drawbacks of Backpropagation 12-3 erformance Surface EXample 12-3 Convergence Example 12-7 Heuristic Modifications of Backpropagation 129 Momentum 12-9 Variable Learning Rate 12-12 Numerical Optimization Techniques 12-14 Conjugate gradient 12-14 Levenberg-Marquardt algorithm 12-19 Summary of Results 12-28 Solved Problems 12-32 Epilogue 12-46 Further Reading 12-47 Exercises 12-50 Generalization 13 Objectives 13-1 Theory and Examples 13-2 Problem statement 13-2 Methods for Improving Generalization 13-5 Estimating generalization error 13-6 Early Stopping 13-6 Regularizati 13-8 Bayesian Analysis 13-10 Bayesian Regularization 13-12 Relationship Between Early Stopping and Regularizat 13-19 Summary of Results 13-29 Solved Problems 13-32 13-44 Further Reading 13-45 Exercises 13-47 14 Dynamic Networks Objectives 14-1 Theory and examples 14-2 Layered Digital Dynamic Networks 14-3 Example dynamic Networks 14-5 Principles of Dynamic Learning 14-8 Dynamic Backpropagation 14-12 Preliminary definitions 14-12 Real Time Recurrent Learning 14-12 Backpropagation-Through-Time 14-22 Summary and comments on Dynamic T raining 14-30 Summary of Results 14-34 Solved Problems 14-37 Epilogue 14-46 Further Reading 14-47 Exercises 14-48 【实例截图】
【核心代码】