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Neural Networks : an Introduction

Author: Berndt Müller; Joachim Reinhardt; Michael T Strickland
Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1995.
Series: Physics of neural networks.
Edition/Format:   eBook : Bibliographic data : EnglishView all editions and formats
Summary:
Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Domany, Eytan.
Neural Networks : An Introduction.
Berlin/Heidelberg : Springer Berlin Heidelberg, ©1995
Material Type: Bibliographic data, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Berndt Müller; Joachim Reinhardt; Michael T Strickland
ISBN: 9783642577604 3642577601
OCLC Number: 840292403
Description: 1 online resource (XV, 330 pages 100 illustrations).
Contents: 1. The Structure of the Central Nervous System --
2. Neural Networks Introduced --
3. Associative Memory --
4. Stochastic Neurons --
5. Cybernetic Networks --
6. Multilayered Perceptrons --
7. Applications --
8. More Applications of Neural Networks --
9. Network Architecture and Generalization --
10. Associative Memory: Advanced Learning Strategies --
11. Combinatorial Optimization --
12. VLSI and Neural Networks --
13. Symmetrical Networks with Hidden Neurons --
14. Coupled Neural Networks --
15. Unsupervised Learning --
16. Evolutionary Algorithms for Learning --
17. Statistical Physics and Spin Glasses --
18. The Hopfield Network for p/N' 0 --
19. The Hopfield Network for Finite p/N --
20. The Space of Interactions in Neural Networks --
21. Numerical Demonstrations --
22. ASSO: Associative Memory --
23. ASSCOUNT: Associative Memory for Time Sequences --
24. PERBOOL: Learning Boolean Functions with Back-Prop --
25. PERFUNC: Learning Continuous Functions with Back-Prop --
26. Solution of the Traveling-Salesman Problem --
27. KOHOMAP: The Kohonen Self-organizing Map --
28. btt: Back-Propagation Through Time --
29. NEUROGEN: Using Genetic Algorithms to Train Networks --
References.
Series Title: Physics of neural networks.
Responsibility: by Berndt Müller, Joachim Reinhardt, Michael T. Strickland.

Abstract:

Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers more advanced subjects such as the statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - In the self-contained final part, seven programs that provide practical demonstrations of neural-network models and their learning strategies are discussed. The software is included on a 3 1/2-inch MS-DOS diskette. The source code can be modified using Borland's TURBO-C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers.

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