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Multistage control of a stochastic system in a fuzzy environment using a genetic algorithm
Author(s) -
Kacprzyk Janusz
Publication year - 1998
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199810/11)13:10/11<1011::aid-int8>3.0.co;2-o
Subject(s) - mathematical optimization , dynamic programming , computer science , sequence (biology) , fuzzy logic , markov chain , fuzzy control system , stochastic control , genetic algorithm , markov decision process , control (management) , optimal control , markov process , mathematics , artificial intelligence , machine learning , statistics , genetics , biology
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. © 1998 John Wiley & Sons, Inc.