A generalized defuzzification method via bad distributions

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Abstract
Defuzzification in fuzzy logic controllers concerns itself with the issue of selecting an appropriate crisp value from the fuzzy output of the controller. We provide a parametized family of defuzzification operations. We call this family BAsic Defuzzification Distributions (BADD). We show that the commonly used methods. Mean of Maximum and Center of Area are special cases of this family. We suggest the use of these BADD transformations form the basis of a learning scheme to obtain the optimal defuzzification method in a given application. We suggest that the parameter in the BADD family, the distinction between different defuzzification methods, is related to the confidence we have in the rest of the controller.

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