<b>Neural Networks </b>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.

Neural Networks: An Introduction (Physics of Neural Networks)

Reinhardt, Joachim

Müller, Berndt

Strickland, Michael T.

Reference

Springer

<p>Here is a thorough introductory text, presented in its fourth edition, providing all the necessary mathematical tools, together with examples and worked problems. In their presentation the authors adopt a heuristic approach based on propagator formalism.</p>

Quantum Electrodynamics

GREINER, WALTER

Computers & Technology

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