Networks of Learning Automata: Techniques for Online Stochastic Optimization

Overview

Networks of Learning Automata: Techniques for Online Shastic 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 shastic 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 ...

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Overview

Networks of Learning Automata: Techniques for Online Shastic 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 shastic 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

  • ISBN-13: 9781461347750
  • Publisher: Springer US
  • Publication date: 12/31/2013
  • Edition description: Softcover reprint of the original 1st ed. 2004
  • Pages: 268
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.60 (d)

Table of Contents

Dedication
Preface
1 Introduction 1
2 Games of Learning Automata 51
3 Feedforward Networks 105
4 Learning Automata for Pattern Classification 139
5 Parallel Operation of Learning Automata 177
6 Some Recent Applications 205
Epilogue 223
App. A The ODE Approach to Analysis of Learning Algorithms 227
App. B Proofs of Convergence for Pursuit Algorithm 241
App. C Weak Convergence and SDE Approximations 247
References 253
Index 265
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