Summarizing the status of the neural network field today, this comprehensive volume presents the software-based paradigms and the hardware implementations of neural networks and how they function. Michael Chester describes the mathematical foundations of the various neural network models, as well as those of fuzzy theory.
Sample applications are described in such areas as optical recognition of handwritten characters, speech recognition, machine control, and simulated biological evolution. Diagrams show neurons, their interconnections, and their behavior, as input patterns are presented to the networks. The step-by-step operations of networks are shown, both in their learning modes and their recall modes, as they categorize new input patterns on the basis of what was learned in the earlier phase.
Neural Networks: A Tutorial provides the ideal bridge from theoretical background to practical understanding of neural networks.

From the Publisher

This book bridges the gap between the highly theoretical level of research in neural networks and the practical, analytical level encountered by those who are not specialized in the neural field.

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