Integrating design stage of fuzzy systems using genetic algorithms

The authors propose an automatic fuzzy system design method that uses a genetic algorithm and integrates three design stages. The method determines membership functions, the number of fuzzy rules, and the rule-consequent parameters at the same time. Because these design stages may not be independent, it is important to consider them simultaneously to obtain optimal fuzzy systems. The method includes a genetic algorithm and a penalty strategy that favors systems with fewer rules. The method was applied to the classic inverted-pendulum control problem and has been shown to be practical through a comparison with another method