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Why do we need q‐rung orthopair fuzzy sets? Some evidence established via mass assignment
Author(s) -
Shaheen Tanzeela,
Ali Muhammad Irfan,
Toor Hamza
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22520
Subject(s) - membership function , fuzzy set , fuzzy logic , set (abstract data type) , computer science , mathematics , function (biology) , fuzzy classification , data mining , fuzzy set operations , type 2 fuzzy sets and systems , artificial intelligence , algorithm , evolutionary biology , biology , programming language
Intuitionistic fuzzy sets (IFSs) have advantage over fuzzy sets and made it possible to describe imprecise information considering its positive and negative aspects simultaneously. In an information system mass assignment and possibility theory are very useful to assign membership grades to elements in a fuzzy set. Unfortunately the situation differs for IFSs in assigning membership function (MF) and nonmembership function (NMF). In this paper, it is shown that the above‐mentioned theories fail to produce the MF and NMF for IFSs. Aim of this paper is to present an alternate algorithm to generate these grading functions based on q‐rung orthopair fuzzy set. Consequently, it will be extremely convenient to model imprecise and vague information using this approach.