A STUDY ON CHARACTERISTICS OF DEFUZZYFICATION METHODS IN FUZZY CONTROL

Defuzzification plays a great role in fuzzy control system. Defuzzification is a process which maps from a space defined over an output universe of discourse into a space of nonfuzzy(crisp) number. But, it's impossible to convert a fuzzy set into a numeric value without losing some information during defuzzification. Also it's very hard to find a number that best represents a fuzzy set. Many methods have been used for defuzzification but most of then were problem dependent. There has been no rule which guides how to select a method that is suitable to solve given problem. Here, we have investigated most widely used methods and we have analyzed their characteristics and evaluated them. D. Driankov and Mizumoto have suggested 5 criteria which the‘ideal’defuzzification method should satisfy. But, they didn't considered about control action. Output fuzzy set if not only a fuzzy set but also a sequence of control action. We suggested 4 new criteria which describe sequence of cont ol action from some experiments. In addition, we have compared each method in simple adaptive fuzzy control. COG(Center of Gravity), or COS(Center of Sums) methods were successful in fuzzy control. However, at transition region, MOM(Mean of Maxima) was best among others in adaptive fuzzy control.