Neural Networks: An Introduction [Paperback]

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.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. I have enjoyedló/