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Digital neural networks

Author: S Y Kung
Publisher: Englewood Cliffs, N.J. : PTR Prentice Hall, ©1993.
Series: Prentice-Hall information and system sciences series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Covering the fundamental theory and practical implementation of various neural models, this text provides a coherent exploration and a well structured presentation of the three most important aspects  Read more...

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Additional Physical Format: Online version:
Kung, S.Y. (Sun Yuan).
Digital neural networks.
Englewood Cliffs, N.J. : PTR Prentice Hall, ©1993
(OCoLC)645827986
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: S Y Kung
ISBN: 0136123260 9780136123262
OCLC Number: 27066578
Description: xviii, 444 pages : illustrations ; 25 cm.
Contents: pt. I. Introduction --
1. Overview --
1.2. Applications, Algorithms, and Architectures --
1.3. Taxonomy of Neural Networks --
pt. II. Unsupervised Models --
2. Fixed-Weight Associative Memory Networks --
2.2. Feedforward Associative Memory Networks --
2.3. Feedback Associative Memory Networks --
3. Competitive Learning Networks --
3.2. Basic Competitive Learning Networks --
3.3. Adaptive Clustering Techniques: VQ and ART --
3.4. Self-Organizing Feature Map: Sensitivity to Neighborhood and History --
3.5. Neocognition: Hierarchically Structured Model --
pt. III. Supervised Models --
4. Decision-Based Neural Networks --
4.2. Linear Perceptron Networks --
4.3. Decision-Based Neural Networks --
4.4. Applications to Signal/Image Classifications --
5. Approximation/Optimization Neural Networks --
5.2. Linear Approximation Networks --
5.3. Nonlinear Multilayer Back-Propagation Networks --
5.4. Training Versus Generalization Performance --
5.5. Applications of Back-Propagation Networks --
pt. IV. Temporal Models --
6. Deterministic Temporal Neural Networks --
6.2. Linear Temporal Dynamic Models --
6.3. Nonlinear Temporal Dynamic Models --
6.4. Prediction-Based Temporal Networks --
7. Stochastic Temporal Networks: Hidden Markov Models --
7.2. From Markov Model to Hidden Markov Model --
7.3. Learning Phase of Hidden Markov Models --
7.4. Retrieving Phase of Hidden Markov Models --
7.5. Applications to Speech, ECG, and Character Recognition --
pt. V. Advanced Topics --
8. Principal Component Neural Networks --
8.2. From Wiener Filtering to PCA --
8.3. Symmetric Principal Component Analysis --
8.4. BP Network for Asymmetric PCA Problems --
8.5. Applications to Signal/Image Processing --
9. Stochastic Annealing Networks for Optimization --
9.2. Stochastic Neural Networks --
9.3. Applications to Combinatorial Optimization and Image Restoration --
9.4. Boltzmann Machine --
pt. VI. Implementation --
10. Architecture and Implementation --
10.2. Mapping Neural Nets to Array Architectures --
10.3. Dedicated Neural Processing Circuits --
10.4. General-Purpose Digital Neurocomputers.
Series Title: Prentice-Hall information and system sciences series.
Responsibility: S.Y. Kung.
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