Principles of Artificial Neural... (V6)

By: Daniel Graupe

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Hardcover | 1 April 2007 | Edition Number 2

At a Glance

Hardcover
320 Pages

Edition Type
Revised

Dimensions(cm)
25.2 x 17.7 x 2.4

Hardcover

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The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Engineering & Technology Energy Technology & Engineering Electrical Engineering Electronics & Communications Engineering Electronics Engineering Booktopia Publisher Services World Scientific Publishing

Acknowledgments	p. vii
Preface to the First Edition	p. ix
Preface to the Second Edition	p. xi
Introduction and Role of Artificial Neural Networks	p. 1
Fundamentals of Biological Neural Networks	p. 5
Basic Principles of ANNs and Their Early Structures	p. 9
Basic Principles of ANN Design	p. 9
Basic Network Structures	p. 10
The Perceptron's Input-Output Principles	p. 11
The Adaline (ALC)	p. 12
The Perceptron	p. 17
The Basic Structure	p. 17
The Single-Layer Representation Problem	p. 22
The Limitations of the Single-Layer Perceptron	p. 23
Many-Layer Perceptrons	p. 24
Perceptron Case Study: Identifying Autoregressive Parameters of a Signal (AR Time Series Identification)	p. 25
The Madaline	p. 37
Madaline Training	p. 37
Madaline Case Study: Character Recognition	p. 39
Back Propagation	p. 59
The Back Propagation Learning Procedure	p. 59
Derivation of the BP Algorithm	p. 59
Modified BP Algorithms	p. 63
Back Propagation Case Study: Character Recognition	p. 65
Back Propagation Case Study: The Exclusive-OR (XOR) Problem (2-Layer BP)	p. 76
Back Propagation Case Study: The XOR Problem - 3 Layer BP Network	p. 94
Hopfield Networks	p. 113
Introduction	p. 113
Binary Hopfield Networks	p. 113
Setting of Weights in Hopfield Nets - Bidirectional Associative Memory (BAM) Principle	p. 114
Walsh Functions	p. 117
Network Stability	p. 118
Summary of the Procedure for Implementing the Hopfield Network	p. 121
Continuous Hopfield Models	p. 122
The Continuous Energy (Lyapunov) Function	p. 123
Hopfield Network Case Study: Character Recognition	p. 125
Hopfield Network Case Study: Traveling Salesman Problem	p. 136
Counter Propagation	p. 161
Introduction	p. 161
Kohonen Self-Organizing Map (SOM) Layer	p. 161
Grossberg Layer	p. 162
Training of the Kohonen Layer	p. 162
Training of Grossberg Layers	p. 165
The Combined Counter Propagation Network	p. 165
Counter Propagation Network Case Study: Character Recognition	p. 166
Adaptive Resonance Theory	p. 179
Motivation	p. 179
The ART Network Structure	p. 179
Setting-Up of the ART Network	p. 183
Network Operation	p. 184
Properties of ART	p. 186
Discussion and General Comments on ART-I and ART-II	p. 186
ART-I Network Case Study: Character Recognition	p. 187
ART-I Case Study: Speech Recognition	p. 201
The Cognitron and the Neocognitron	p. 209
Background of the Cognitron	p. 209
The Basic Principles of the Cognitron	p. 209
Network Operation	p. 209
Cognitron's Network Training	p. 211
The Neocognitron	p. 213
Statistical Training	p. 215
Fundamental Philosophy	p. 215
Annealing Methods	p. 216
Simulated Annealing by Boltzman Training of Weights	p. 216
Stochastic Determination of Magnitude of Weight Change	p. 217
Temperature-Equivalent Setting	p. 217
Cauchy Training of Neural Network	p. 217
Statistical Training Case Study - A Stochastic Hopfield Network for Character Recognition	p. 219
Statistical Training Case Study: Identifying AR Signal Parameters with a Stochastic Perceptron Model	p. 222
Recurrent (Time Cycling) Back Propagation Networks	p. 233
Recurrent/Discrete Time Networks	p. 233
Fully Recurrent Networks	p. 234
Continuously Recurrent Back Propagation Networks	p. 235
Recurrent Back Propagation Case Study: Character Recognition	p. 236
Large Scale Memory Storage and Retrieval (LAMSTAR) Network	p. 249
Basic Principles of the LAMSTAR Neural Network	p. 249
Detailed Outline of the LAMSTAR Network	p. 251
Forgetting Feature	p. 257
Training vs. Operational Runs	p. 258
Advanced Data Analysis Capabilities	p. 259
Correlation, Interpolation, Extrapolation and Innovation-Detection	p. 261
Concluding Comments and Discussion of Applicability	p. 262
LAMSTAR Network Case Study: Character Recognition	p. 265
Application to Medical Diagnosis Problems	p. 280
Problems	p. 285
References	p. 291
Author Index	p. 299
Subject Index	p. 301
Table of Contents provided by Ingram. All Rights Reserved.

Principles of Artificial Neural... (V6)

At a Glance

Hardcover

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How to return your order

Defective items

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