MULTISTAGE CONTROL OF A STOCHASTIC-SYSTEM IN A FUZZY ENVIRONMENT USING A GENETIC ALGORITHM

We consider the classic Bellman and Zadeh multistage control problem u nder fuzzy constraints imposed on applied controls and fuzzy goals imp osed on attained states with a stochastic system under control that is assumed to be a Markov chain. An optimal sequence of controls is soug ht 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 th e traditionally employed Bellman and Zadeh dynamic programming. (C) 19 98 John Wiley & Sons, Inc.