An Enhanced Adaptive Fuzzy Membership Function for Intuitionistic Fuzzy Sets

Abstract

The Enhanced Adaptive Fuzzy Membership Function proposed in this article provides a new approach to determine the membership/non-membership levels in an uncertainty interval, which consist of lower bound and upper bound. Proposed for addressing the intuitionistic fuzzy systems, the EAFMF essentially accommodates membership, non-membership as well as hesitancy measures and thus expresses a broader spectrum of uncertainty. Symmetrical architecture is well capable to represent skewness, nonlinearity and expanded fuzzification regions as compared with rectangle type membership functions. The centroid method applied in our model gives a more stable and representative solution for the crisp outputs computed from the uncertainty interval LB-UB. The proposed approach is well suited to the needs of such applications as medical diagnosis, preventive maintenance, and support for decision making where an accurate reading and interpretation of indecisive information plays a crucial role.