Learning fuzzy rules for controllers with genetic algorithms

Abstract

A genetic algorithm (GA)-based scheme for learning fuzzy rules for controllers, called an optimized fuzzy logic controller (OFLC) was proposed by Chan, Xie and Rad (2000). In this article we first analyze their OFLC and discuss some of its limitations. We also propose some modifications on an OFLC to eliminate those limitations. Then, for systems with symmetrical rule base we propose a new method to reduce the number of rules, which reduces the search space as well as the design time. We define a fitness function that reduces the number of rules maintaining the performance of the rule set. The trade-off between the number of rules and the performance can be decided by changing the parameters of our fitness function. We theoretically analyzed the properties of “one-step change mutation” and compared that with our mutation scheme. The proposed scheme, for the inverted pendulum problem can find rule sets containing <5% of all possible fuzzy rules, having good integral time absolute error (ITAE) and it takes only a few steps to balance the system over the entire input space. To show the superiority of our scheme, we compare it with other methods. © 2003 Wiley Periodicals, Inc.