Revistes Catalanes amb Accés Obert (RACO)

A genetic algorithm for the multistage control of a fuzzy system in a fuzzy environment

Janusz Kacprzyk

Resum


We discuss a prescriptive approach to multistage optimal fuzzy control of a
fuzzy system, given by a fuzzy state transition equation. Fuzzy constraints
and fuzzy goals at consecutive control stages are given, and their
confluence, Bellman and Zadeh's fuzzy decision, is an explicit performance
function to be optimized. First, we briefly survey previous basic solution
methods of dynamic programming (Baldwin and Pilsworth, 1982) and
branch-and-bound (Kacprzyk, 1979), which are plagued by low numerical
efficiency, and then sketch Kacprzyk's (1993a--e, 1994a) approach based on
possibilistic interpolative reasoning aimed at enhancing the numerical
efficiency but requiring a solution of a simplified auxiliary problem, and
then some readjustment of the solution obtained.
We propose a genetic algorithm for solving the problem considered. Real
coding and specially defined operations of crossover, mutation, etc. are
employed. The approach yields good results, and is quite efficient
numerically

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