Laurene V. Fausett -
Fundamentals of Neural Networks: Architectures,
Algorithms, and Applications
Brief Description:
Written with the beginner in mind, this volume offers an exceptionally clear
and thorough introduction to neural networks at an elementary level. Systematic
discussion of all major neural nets features presentation of the architectures,
detailed algorithms, and examples of simple applications - in many cases
variations on a theme. Each chapter concludes with suggestions for further study,
including numerous exercises and computer projects. An instructor's manual with
solutions and sample software (in Fortran and C) will be available later this
spring.
Table of Contents
Chapter 1 INTRODUCTION;
1.1 Why neural networks, and why now?;
1.2 What is a neural net?;
1.3 Where are neural nets being used?;
1.4 How are neural networks used?;
1.5 Who is developing neural networks?;
1.6 When neural nets began - the McCulloch-Pitts neuron.
Chapter 2 SIMPLE NEURAL NETS FOR PATTERN CLASSIFICATION;
2.1 General discussion;
2.2 Hebb net;
2.3 Perceptron;
2.4 Adaline.
Chapter 3 PATTERN ASSOCIATION;
3.1 Training algorithms for pattern association;
3.2 Heteroassociative memory neural network;
3.3 Autoassociative net;
3.4 Iterative autoassociative net;
3.5 Bidirectional associative memory (BAM).
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Chapter 4 NEURAL NETWORKS BASED ON COMPETITION;
4.1 Fixed-weight competitive nets;
4.2 Kohonen self-organizing maps;
4.3 Learning vector quantization;
4.4 Counterpropagation.
Chapter 5 ADAPTIVE RESONANCE THEORY;
5.1 Introduction;
5.2 ART1;
5.3 ART2.
Chapter 6 BACKPROPAGATION NEURAL NET;
6.1 Standard backpropagation;
6.2 Variations;
6.3 Theoretical results.
Chapter 7 A SAMPLER OF OTHER NEURAL NETS;
7.1 Fixed weight nets for constrained optimization;
7.2 A few more nets that learn;
7.3 Adaptive architectures;
7.4 Neocognitron.
Glossary; References; Index.
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Laurene V. Fausett - Fundamentals of Neural Networks:
Architectures, Algorithms, and Applications, Prentice Hall.
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