Cellular Automata, Dynamical Systems and Neural Networks / Edition 1

Cellular Automata, Dynamical Systems and Neural Networks / Edition 1

ISBN-10:
0792327721
ISBN-13:
9780792327721
Pub. Date:
03/31/1994
Publisher:
Springer Netherlands
ISBN-10:
0792327721
ISBN-13:
9780792327721
Pub. Date:
03/31/1994
Publisher:
Springer Netherlands
Cellular Automata, Dynamical Systems and Neural Networks / Edition 1

Cellular Automata, Dynamical Systems and Neural Networks / Edition 1

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Overview

This book contains the courses given at the Third School on Statistical Physics and Cooperative Systems held at Santiago, Chile, from 14th to 18th December 1992. The main idea of this periodic school was to bring together scientists work­ with recent trends in Statistical Physics. More precisely ing on subjects related related with non linear phenomena, dynamical systems, ergodic theory, cellular au­ tomata, symbolic dynamics, large deviation theory and neural networks. Scientists working in these subjects come from several areas: mathematics, biology, physics, computer science, electrical engineering and artificial intelligence. Recently, a very important cross-fertilization has taken place with regard to the aforesaid scientific and technological disciplines, so as to give a new approach to the research whose common core remains in statistical physics. Each contribution is devoted to one or more of the previous subjects. In most cases they are structured as surveys, presenting at the same time an original point of view about the topic and showing mostly new results. The expository text of Fran

Product Details

ISBN-13: 9780792327721
Publisher: Springer Netherlands
Publication date: 03/31/1994
Series: Mathematics and Its Applications , #282
Edition description: 1994
Pages: 192
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

Cellular Automata and Transducers. A Topological View.- Automata Network Models of Interacting Populations.- Entropy, Pressure and Large Deviation.- Formal Neural Networks: from Supervised to Unsupervised Learning.- Storage of Correlated Patterns in Neural Networks.
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