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140122 ||| eng |
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|a 9783642577604
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| 100 |
1 |
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|a Müller, Berndt
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| 245 |
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|a Neural Networks
|h Elektronische Ressource
|b An Introduction
|c by Berndt Müller, Joachim Reinhardt, Michael T. Strickland
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| 250 |
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|a 2nd ed. 1995
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 1995, 1995
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| 300 |
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|a XV, 331 p
|b online resource
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| 505 |
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|a 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
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| 653 |
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|a Neurosciences
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| 653 |
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|a Neurosciences
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| 653 |
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|a Statistical Physics and Dynamical Systems
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| 653 |
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|a Statistical physics
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Complex Systems
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Dynamical systems
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| 700 |
1 |
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|a Reinhardt, Joachim
|e [author]
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| 700 |
1 |
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|a Strickland, Michael T.
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Physics of Neural Networks
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-642-57760-4?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a 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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