Self-Learning Control of Finite Markov Chains (Automation and Control Engineering)

✍ Scribed by A.S. Poznyak, Kaddour Najim, E. Gomez-Ramirez

Publisher: CRC Press
Year: 2000
Tongue: English
Leaves: 315
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.

✦ Table of Contents

Cover
......Page 1
Series Introduction......Page 8
Preface......Page 10
Contents......Page 14
1:
Controlled Markov Chains......Page 18
Part I:
Unconstrained Markov Chains......Page 62
2:
Lagrange Multipliers Approach......Page 64
3:
Penalty Function Approach......Page 86
4:
Projection Gradient Method......Page 104
Part II:
Constrained Markov Chains......Page 132
5:
Lagrange Multipliers Approach......Page 134
6:
Penalty Function Approach......Page 158
7: Nonregular Markov Chains......Page 184
8:
Practical AsPects......Page 206
Appendix A......Page 282
Appendix B......Page 298
Index......Page 314

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