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Fundamentals of neural networks : architectures, algorithms, and applications /

Guardado en:
Autor Principal: Fausett, Laurene.
Formato: Libro
Idioma:English
Publicado: Englewood Cliffs, NJ : Prentice-Hall, c1994.
Materias:
Redes neuronales (Computación)
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Tabla de Contenidos:
  • 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.

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