Recursive neural networks for associative memory
著者
書誌事項
Recursive neural networks for associative memory
（Wiley-Interscience series in systems and optimization）
Wiley, c1990
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注記
Includes bibliographical references (p. 179-189) and index
内容説明・目次
内容説明
Titles of related interest Simulated Annealing and Boltzmann Machines Emile Aarts, Philips Research Laboratories, Eindhoven, and Eindhoven University of Technology, The Netherlands Jan Korst, Philips Research Laboratories, Eindhoven, The Netherlands Simulated annealing is a solution method in the field of combinatorial optimization based on a simulation of the physical process of annealing. A substantial reduction of the computational effort required to use this method may be achieved by using a computational model based on massively parallel execution, such as the Boltzmann machine, which is a neural network model. This book is intended as an introduction to the theory and applications of simulated annealing and Boltzmann machines. It will be of great interest to students and researchers in combinatorial optimization and neural networks, as well as to all those using optimization techniques in practice. 1988 The Metaphorical Brain 2, Neural Networks and Beyond Michael A.
Arbib, Program in Neural, Informational and Behavioral Sciences, University of Southern California, USA This book combines two exciting quests, the quest to understand the workings of the human brain and the quest to build intelligent machines. It shows how each quest can provide insights vital to the success of the other. It develops basic ideas about neural networks, both artificial and biological, and introduces the language of schema theory to describe the distributed interactions that underlie intelligence in the brain of human, animal or robot. It reaffirms the paradigm of highly distributed cooperative computation, showing how it not only deepens our understanding of human mind/brain, but also catalyzes the development of a new generation of computing machinery. The book presents many new results, both from my own group and elsewhere, that have enriched that paradigm during the last fifteen years.
The book as a whole, although by no means light reading, should be accessible overall to anyone who reads Scientific American; but it is hoped that much of the material merits the attention not only of "the intelligent laymen" but also of experts and serious students of artificial intelligence, neural networks, robotics, cognitive science, or neuroscience'. - From the Author's Preface 1989
目次
Principles, Problems and Approaches. The Deterministic Approach. The Statistical Approach. Thermodynamic Extension. Higher Order Networks. Network Design. Bibliography. Index.
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