Fundamentals of neural networks : architectures, algorithms, and applications
Laurene Fausett.
Englewood Cliffs, NJ : Prentice-Hall, ©1994.
xvi, 461 págs. : ilustraciones ; 23 cm.
ISBN: 0133341860
Incluye referencias bibliográficas (p. 437-447) e índice.
Contenido
- Ch. 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
- Ch. 2. Simple Neural Nets for Pattern Classification
- 2.1. General Discussion
- 2.2. Hebb Net
- 2.3. Perceptron
- 2.4. Adaline
- Ch. 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)
- Ch. 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
- Ch. 5. Adaptive Resonance Theory
- 5.1. Introduction
- 5.2. Art1
- 5.3. Art2
- Ch. 6. Backpropagation Neural Net
- 6.1. Standard Backpropagation
- 6.2. Variations
- 6.3. Theoretical Results
- Ch. 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.
