Principles of artificial neural networks

Title:

Personal Author:

Graupe, Daniel

Series:

Advanced series on circuits and systems ; 6

Edition:

2nd ed.

Publication Information:

Singapore : World Scientific, 2007

ISBN:

9789812706249

Subject Term:

Artificial neural networks

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010167792	QA76.87 G72 2007	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	30000010215441	QA76.87 G72 2007	Open Access Book	Book	Searching... Unknown

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Acknowledgments	p. vii
Preface to the First Edition	p. ix
Preface to the Second Edition	p. xi
Chapter 1 Introduction and Role of Artificial Neural Networks	p. 1
Chapter 2 Fundamentals of Biological Neural Networks	p. 5
Chapter 3 Basic Principles of ANNs and Their Early Structures	p. 9
3.1 Basic Principles of ANN Design	p. 9
3.2 Basic Network Structures	p. 10
3.3 The Perceptron's Input-Output Principles	p. 11
3.4 The Adaline (ALC)	p. 12
Chapter 4 The Perceptron	p. 17
4.1 The Basic Structure	p. 17
4.2 The Single-Layer Representation Problem	p. 22
4.3 The Limitations of the Single-Layer Perceptron	p. 23
4.4 Many-Layer Perceptrons	p. 24
4.A Perceptron Case Study: Identifying Autoregressive Parameters of a Signal (AR Time Series Identification)	p. 25
Chapter 5 The Madaline	p. 37
5.1 Madaline Training	p. 37
5.A Madaline Case Study: Character Recognition	p. 39
Chapter 6 Back Propagation	p. 59
6.1 The Back Propagation Learning Procedure	p. 59
6.2 Derivation of the BP Algorithm	p. 59
6.3 Modified BP Algorithms	p. 63
6.A Back Propagation Case Study: Character Recognition	p. 65
6.B Back Propagation Case Study: The Exclusive-OR (XOR) Problem (2-Layer BP)	p. 76
6.C Back Propagation Case Study: The XOR Problem - 3 Layer BP Network	p. 94
Chapter 7 Hopfield Networks	p. 113
7.1 Introduction	p. 113
7.2 Binary Hopfield Networks	p. 113
7.3 Setting of Weights in Hopfield Nets - Bidirectional Associative Memory (BAM) Principle	p. 114
7.4 Walsh Functions	p. 117
7.5 Network Stability	p. 118
7.6 Summary of the Procedure for Implementing the Hopfield Network	p. 121
7.7 Continuous Hopfield Models	p. 122
7.8 The Continuous Energy (Lyapunov) Function	p. 123
7.A Hopfield Network Case Study: Character Recognition	p. 125
7.B Hopfield Network Case Study: Traveling Salesman Problem	p. 136
Chapter 8 Counter Propagation	p. 161
8.1 Introduction	p. 161
8.2 Kohonen Self-Organizing Map (SOM) Layer	p. 161
8.3 Grossberg Layer	p. 162
8.4 Training of the Kohonen Layer	p. 162
8.5 Training of Grossberg Layers	p. 165
8.6 The Combined Counter Propagation Network	p. 165
8.A Counter Propagation Network Case Study: Character Recognition	p. 166
Chapter 9 Adaptive Resonance Theory	p. 179
9.1 Motivation	p. 179
9.2 The ART Network Structure	p. 179
9.3 Setting-Up of the ART Network	p. 183
9.4 Network Operation	p. 184
9.5 Properties of ART	p. 186
9.6 Discussion and General Comments on ART-I and ART-II	p. 186
9.A ART-I Network Case Study: Character Recognition	p. 187
9.B ART-I Case Study: Speech Recognition	p. 201
Chapter 10 The Cognitron and the Neocognitron	p. 209
10.1 Background of the Cognitron	p. 209
10.2 The Basic Principles of the Cognitron	p. 209
10.3 Network Operation	p. 209
10.4 Cognitron's Network Training	p. 211
10.5 The Neocognitron	p. 213
Chapter 11 Statistical Training	p. 215
11.1 Fundamental Philosophy	p. 215
11.2 Annealing Methods	p. 216
11.3 Simulated Annealing by Boltzman Training of Weights	p. 216
11.4 Stochastic Determination of Magnitude of Weight Change	p. 217
11.5 Temperature-Equivalent Setting	p. 217
11.6 Cauchy Training of Neural Network	p. 217
11.A Statistical Training Case Study - A Stochastic Hopfield Network for Character Recognition	p. 219
ll.B Statistical Training Case Study: Identifying AR Signal Parameters with a Stochastic Perceptron Model	p. 222
Chapter 12 Recurrent (Time Cycling) Back Propagation Networks	p. 233
12.1 Recurrent/Discrete Time Networks	p. 233
12.2 Fully Recurrent Networks	p. 234
12.3 Continuously Recurrent Back Propagation Networks	p. 235
12.A Recurrent Back Propagation Case Study: Character Recognition	p. 236
Chapter 13 Large Scale Memory Storage and Retrieval (LAMSTAR) Network	p. 249
13.1 Basic Principles of the LAMSTAR Neural Network	p. 249
13.2 Detailed Outline of the LAMSTAR Network	p. 251
13.3 Forgetting Feature	p. 257
13.4 Training vs. Operational Runs	p. 258
13.5 Advanced Data Analysis Capabilities	p. 259
13.6 Correlation, Interpolation, Extrapolation and Innovation-Detection	p. 261
13.7 Concluding Comments and Discussion of Applicability	p. 262
13.A LAMSTAR Network Case Study: Character Recognition	p. 265
13.B Application to Medical Diagnosis Problems	p. 280
Problems	p. 285
References	p. 291
Author Index	p. 299
Subject Index	p. 301

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Table of Contents