Open AccessCombining Fuzzy Logic and Dempster-Shafer Theory
Author(s) -
Andino Maseleno,
Md. Mahmud Hasan,
Norjaidi Tuah
Publication year - 2015
Publication title -
telkomnika: indonesian journal of electrical engineering/telkomnika
Language(s) - English
Resource type - Journals
eISSN - 2460-7673
pISSN - 2302-4046
DOI - 10.11591/tijee.v16i3.1651
Subject(s) - dempster–shafer theory , fuzzy logic , artificial intelligence , ignorance , membership function , similarity (geometry) , mathematical theory , novelty , computer science , mathematics , function (biology) , t norm fuzzy logics , fuzzy number , fuzzy set , type 2 fuzzy sets and systems , defuzzification , data mining , philosophy , physics , theology , epistemology , quantum mechanics , evolutionary biology , image (mathematics) , biology
This research aims to combine the mathematical theory of evidence with the rule based logics to refine the predictable output. Integrating Fuzzy Logic and Dempster-Shafer theory by calculating the similarity between Fuzzy membership function. The novelty aspect of this work is that basic probability assignment is proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get a basic probability assignment. The Dempster-Shafer mathematical theory of evidence has attracted considerable attention as a promising method of dealing with some of the basic problems arising in combination of evidence and data fusion. Dempster-Shafer theory provides the ability to deal with ignorance and missing information. The foundation of Fuzzy logic is natural language which can help to make full use of expert information.