TY - JOUR

T1 - An Interactive Algorithm to Construct an Appropriate Nonlinear Membership Function Using Information Theory and Statistical Method

AU - Hasuike, Takashi

AU - Katagiri, Hideki

AU - Tsubaki, Hiroe

N1 - Publisher Copyright:
© 2015 The Authors. Published by Elsevier B.V.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

PY - 2015

Y1 - 2015

N2 - This paper develops a constructing algorithm for an appropriate membership function as objectively as possible. It is important to set an appropriate membership function for real-world decision making. The main academic contribution of our proposed algorithm is to integrate a general continuous and nonlinear function with fuzzy Shannon entropy into subjective interval estimation by a heuristic method under a given probability density function based on real-world data. Two main steps of our proposed approach are to set membership values a decision maker confidently judges whether an element is included in the given set or not and to obtain other values objectively by solving a mathematical programming problem with fuzzy Shannon entropy. It is difficult to solve the problem efficiently using previous constructing approaches due to nonlinear function. In this paper, the given nonlinear membership function is approximately transformed into a piecewise linear membership function, and the appropriate values are determined. Furthermore, by introducing natural assumptions in the real-world and interactively adjusting the membership values, an algorithm to obtain the optimal condition of each appropriate membership value is developed.

AB - This paper develops a constructing algorithm for an appropriate membership function as objectively as possible. It is important to set an appropriate membership function for real-world decision making. The main academic contribution of our proposed algorithm is to integrate a general continuous and nonlinear function with fuzzy Shannon entropy into subjective interval estimation by a heuristic method under a given probability density function based on real-world data. Two main steps of our proposed approach are to set membership values a decision maker confidently judges whether an element is included in the given set or not and to obtain other values objectively by solving a mathematical programming problem with fuzzy Shannon entropy. It is difficult to solve the problem efficiently using previous constructing approaches due to nonlinear function. In this paper, the given nonlinear membership function is approximately transformed into a piecewise linear membership function, and the appropriate values are determined. Furthermore, by introducing natural assumptions in the real-world and interactively adjusting the membership values, an algorithm to obtain the optimal condition of each appropriate membership value is developed.

KW - Fuzzy entropy

KW - Interactive algorithm

KW - Mathematical programming

UR - http://www.scopus.com/inward/record.url?scp=84962667098&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962667098&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2015.09.140

DO - 10.1016/j.procs.2015.09.140

M3 - Conference article

AN - SCOPUS:84962667098

VL - 61

SP - 32

EP - 37

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

T2 - Complex Adaptive Systems, 2015

Y2 - 2 November 2015 through 4 November 2015

ER -