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1st Edition
Self-Learning Control of Finite Markov Chains
Copyright Year 2000
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Book Description
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
Controlled Markov chains. Unconstrained Markov chains: Lagrange multipliers approach; penalty function approach; projection gradient method. Constrained Markov chains: Lagrange multipliers approach; penalty function approach; nonregular Markov chains; practical aspects.
Author(s)
Biography
Poznyak, A.S.; Najim, Kaddour; Gomez-Ramirez, E.