Evidence Theory in Picture Fuzzy Set Environment
Author(s) -
Harish Garg,
R. Sujatha,
D. Nagarajan,
J. Kavikumar,
Jeonghwan Gwak
Publication year - 2021
Publication title -
journal of mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.252
H-Index - 13
eISSN - 2314-4785
pISSN - 2314-4629
DOI - 10.1155/2021/9996281
Subject(s) - mathematics , type 2 fuzzy sets and systems , fuzzy set , generalization , fuzzy number , fuzzy logic , membership function , belief structure , fuzzy set operations , fuzzy measure theory , fuzzy classification , interval (graph theory) , dempster–shafer theory , defuzzification , set (abstract data type) , artificial intelligence , data mining , computer science , mathematical economics , statistics , combinatorics , mathematical analysis , programming language
Picture fuzzy set is the most widely used tool to handle the uncertainty with the account of three membership degrees, namely, positive, negative, and neutral such that their sum is bound up to 1. It is the generalization of the existing intuitionistic fuzzy and fuzzy sets. This paper studies the interval probability problems of the picture fuzzy sets and their belief structure. The belief function is a vital tool to represent the uncertain information in a more effective manner. On the other hand, the Dempster–Shafer theory (DST) is used to combine the independent sources of evidence with the low conflict. Keeping the advantages of these, in the present paper, we present the concept of the evidence theory for the picture fuzzy set environment using DST. Under this, we define the concept of interval probability distribution and discuss its properties. Finally, an illustrative example related to the decision-making process is employed to illustrate the application of the presented work.
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