Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms
IEEE transactions on fuzzy systems
This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially,
... design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules. ' ROMAlFAR AND MCCORMICK: MEMBERSHIP €?UNCTIONS AND RULE SETS FOR FUZZY CONTROLLERS 139 Abdollah Homaifar received the B.S. and M.S. degrees from State University of New York at Stony Brook in 1979, and 1980, respectively, and the Ph.D. degree from the University of Alabama in 1987, all in electrical engineering. He is currently an Assistant Professor in the Department of Electrical Engineering at the North Carolina A&T State University. His current research interests include the application of genetic algorithms and fuzzy logic to the design of a controller for a high speed civil transport vehicle as well as machine learning, expert systems, adaptive control, optimal control, signal processing, and fuzzy control and modeling.