Networks of Learning Automata - Techniques for Online Stochastic Optimization (Paperback, Softcover reprint of the original 1st ed. 2004)

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Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions. Mathematical analysis of the behavior of games and feedforward networks is provided. Algorithms considered here can be used for online optimization of systems based on noisy measurements of performance index. Also, algorithms that assure convergence to the global optimum are presented. Parallel operation of automata systems for improving speed of convergence is described. The authors also include extensive discussion of how learning automata solutions can be constructed in a variety of applications.

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Product Description

Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions. Mathematical analysis of the behavior of games and feedforward networks is provided. Algorithms considered here can be used for online optimization of systems based on noisy measurements of performance index. Also, algorithms that assure convergence to the global optimum are presented. Parallel operation of automata systems for improving speed of convergence is described. The authors also include extensive discussion of how learning automata solutions can be constructed in a variety of applications.

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Product Details

General

Imprint

Springer-Verlag New York

Country of origin

United States

Release date

September 2012

Availability

Expected to ship within 10 - 15 working days

First published

2004

Authors

,

Dimensions

235 x 155 x 15mm (L x W x T)

Format

Paperback

Pages

268

Edition

Softcover reprint of the original 1st ed. 2004

ISBN-13

978-1-4613-4775-0

Barcode

9781461347750

Categories

LSN

1-4613-4775-0



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