Title :
Adaptive fuzzy control: a GA approach
Abstract :
This paper presents practical approach in design and implementing an adaptive fuzzy control system, GA FuzzyWare (GAF), that utilizes genetic algorithm (GA) as the adaptation engine. We examine the fuzzy control side of the GAF system, which uses four-point fuzzy membership set for its efficiency on control environment. A three-phased inference engine is used for preprocess, fuzzy inference, clad postprocess. GAF also provides the capability to automatically emulate a system based on its data set. To eliminate the problem of tuning fuzzy sets and fuzzy rules that are common to many fuzzy systems, GAF uses GA to adapt the fuzzy control system. This paper, discusses how GAF applies genetic algorithm to adapt fuzzy rule based systems and the details of adaptation operators
Keywords :
fuzzy control; fuzzy systems; genetic algorithms; inference mechanisms; knowledge based systems; GA FuzzyWare; adaptation operators; adaptive fuzzy control; clad postprocess; fuzzy membership set; fuzzy rules; fuzzy sets; genetic algorithm; three-phased inference engine; Adaptive control; Adaptive systems; Algorithm design and analysis; Automatic control; Engines; Fuzzy control; Fuzzy sets; Fuzzy systems; Genetic algorithms; Programmable control;
Conference_Titel :
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3645-3
DOI :
10.1109/FUZZY.1996.552359
