Fausett, Laurene.
Fundamentals of neural networks : architectures, algorithms, and applications / Laurene Fausett.
— Englewood Cliffs, NJ : Prentice-Hall, c1994. xvi, 461 p. : il. ; 23 cm.
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.
ISBN 0133341860
|