Achieving the best defuzzifier in terminology of Persian sentences through classified fuzzy method

Abstract

Fuzzy logic is a multi-valued concept, whose emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of [0,1]. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. For that purpose, we first obtained the best classification for each defuzzifier, based on which a classified fuzzy was implemented. Nonetheless, the fuzzy system used in this research was classified based on statistical computations. To achieve the best classification, five defuzzification methods (Mean Of Max, Max Of Membership, Largest Of Max, Smallest Of Max, and Central Average) competed in 16 roles each in five classes (different matrices). Finally, Mean of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among 5 different defuzzification methods.