Evaluation of approximate Fuzzy Membership Function using Linguistic Input-an Approached Based on Cubic Spline
Viewed = 64 time(s)
Fuzzy logic systems have found extensive use in system identification, decision making, and pattern recognition problems from industries to academics. The membership functions play a pivotal role in overall role in fuzzy representation, as these are considered as the building blocks of fuzzy set theory and they decide the degree of truth in fuzzy logic. The extraction of the membership function is ambience dependent and thus complication exists in the process of evaluation. In this assessment the main work deals with the derivation of fuzzy membership function where numerical data is available. The numerical cubic spline and defuzzification technique are used here. In this paper we mainly used triangular fuzzy number to construct the membership function. A case study is furnished to emphasize the advantage of adopting the method.
Copyright (c) 2020 Stabania Chowdhur, Rahul Kar
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.