NEURAL NETWORK DESIGN
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This book 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.
Features:
Extensive coverage of performance learning, including the Widrow-Hoff rule, back propagation, and several enhancements of back propagation (e.g., conjugate gradient, Levenberg-Marquardt variations)
Discussion of recurrent associative memory networks (e.g., Hopfield network)
Detailed examples and numerous solved problems
Associative and competitive networks (including feature maps, learning vector quantization, and adaptive resonance theory) are explained using simple building blocks
Neural Network Design Demonstrations on bound-in disk using MATLAB 4.0 (student and professional versions)
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