Premium
A generalized defuzzification method via bad distributions
Author(s) -
Filev Dimitar P.,
Yager Ronald R.
Publication year - 1991
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550060702
Subject(s) - defuzzification , fuzzy logic , mathematics , fuzzy control system , fuzzy number , controller (irrigation) , artificial intelligence , fuzzy set , computer science , control theory (sociology) , control (management) , agronomy , biology
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 BA sic D efuzzification D istributions (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.