A New Paradigm For Generation Of Fuzzy Membership Function

Abstract

A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1. The input space is sometimes referred to as the universe of discourse. This paper further develops the fuzzy-based algorithm to add the feature of automatic membership function generation in the fuzzy logic module of the algorithm. From this context, a short review of related work in membership function generation is given, and rules associated with it have been incorporated. In this paper, a one step ahead to the nature of the fuzzy logic-based design, a fitness finding method has been proposed. This paper also evaluates the proposed algorithm for deriving membership function based on association rule using control parameters with its implementation. The algorithm is applied by considering a case study of share market data and results are analyzed and compared with the intuitive cases