Neural Networks
An Introduction
2nd Edition
Published on 2. October 1995
Book
Paperback/Softback
XV, 331 pages
978-3-540-60207-1 (ISBN)
Description
Neural Networks
presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like 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. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
Reviews / Votes
"I have enjoyed using the previous edition of this well-known book both as a personal text and as a class manual. Although it claims to be only an introduction, it contains a wealth of material and addresses real problems in physics." Computing ReviewsMore details
Series
Edition
Second Edition 1995
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Professional/practitioner
Edition type
Revised edition
Illustrations
XV, 331 p. With online files/update.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 19 mm
Weight
528 gr
ISBN-13
978-3-540-60207-1 (9783540602071)
DOI
10.1007/978-3-642-57760-4
Schweitzer Classification
Other editions
Previous edition
Book
07/1991
Springer
€85.55
Article exhausted; check for reprint
Content
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.