Population structure of random signal-based learning for a fuzzy logic controller design

摘要

This paper proposes a population structure of random signal-based learning (PRSL), merged with simulated annealing (SA), to optimize the fuzzy logic controller (FLC). Random signal-based learning (RSL) exploits (local search) the search space very well, but it can not explore (global search) the search space because of its serial nature. To overcome these difficulties, PRSL, which consists of serial RSL as a population, was considered. Moreover, SA was added to RSL to help the exploration. The validity of the proposed algorithm was conformed by applying it to the optimization of a FLC for the inverted pendulum.