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Multistage control of a stochastic system in a fuzzy environment using a genetic algorithm

✍ Scribed by Janusz Kacprzyk

Publisher: John Wiley and Sons
Year: 1998
Tongue: English
Weight: 107 KB
Volume: 13
Category: Article
ISSN: 0884-8173
DOI: 10.1002/(sici)1098-111x(199810/11)13:10/11<1011::aid-int8>3.0.co;2-o

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✦ Synopsis

We consider the classic Bellman and Zadeh multistage control problem under fuzzy constraints imposed on applied controls and fuzzy goals imposed on attained states with a stochastic system under control that is assumed to be a Markov chain. An optimal sequence of controls is sought that maximizes the probability of attaining the fuzzy goal subject to the fuzzy constraints over a finite, fixed, and specified planning horizon. A genetic algorithm is shown to be a viable alternative to the traditionally employed Bellman and Zadeh dynamic programming.

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