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Fundamentals of neural networks : architectures, algorithms, and applications / Laurene Fausett.

By: Material type: TextTextPublication details: Englewood Cliffs, NJ : Prentice-Hall, c1994.Description: xvi, 461 p. : ill. ; 24 cmISBN:
  • 0133341860
Subject(s):
Contents:
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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Item type Current library Home library Collection Shelving location Call number Status Date due Barcode Item holds
Open Collection Open Collection FIRST CITY UNIVERSITY COLLEGE FIRST CITY UNIVERSITY COLLEGE Open Collection FCUC Library 006.32 FAU (Browse shelf(Opens below)) Available 00005661
Green Spot Green Spot FIRST CITY UNIVERSITY COLLEGE FIRST CITY UNIVERSITY COLLEGE Green Spot FCUC Library 006.32 FAU (Browse shelf(Opens below)) Available 00005660
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Includes bibliographical references (p. 437-447) and index.

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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