Neural Network Design / Edition 1by Howard B. Demuth
Pub. Date: 02/28/1996
Neural Network Design provides a clear and detailed survey of basic neural network architectures and learning rules. In it, the authors emphasize mathematical analysis of networks, methods for training networks, and application of networks to practical engineering problems in pattern recognition, signal processing, and control systems. The book incorporates necessary background material (such as linear algebra, optimization, and stability), while including extensive coverage of performance learning, like the Widrow-Hoff rule and back propagation. The authors describe several enhancements of back propagation, such as the conjugate gradient and Levenberg-Marquardt variations.
Table of ContentsPreface. 1. Introduction. 2. Neuron Model and Network Architectures. 3. An Illustrative Example. 4. Perception Learning Rule. 5. Signal and Weight Vector Spaces. 6. Linear Transformations for Neural Networks. 7. Supervised Hebbian Learning. 8. Performance Surfaces and Optimum Points. 9. Performance Optimization. 10. Widrow-Hoff Learning. 11. Backpropagation. 12. Variations on Backpropagation. 13. Associative Learning. 14. Competitive Networks. 15. Grossberg Network. 16. Adaptive Resonance Theory. 17. Stability. 18. Hopfield Network. 19. Epilogue. Appendices. Index.
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