Why do we need q‐rung orthopair fuzzy sets? Some evidence established via mass assignment | Zendy

Shaheen Tanzeela | Zendy; Ali Muhammad Irfan | Zendy; Toor Hamza | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

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 , computer science , fuzzy classification , fuzzy set operations , mathematics , set (abstract data type) , type 2 fuzzy sets and systems , data mining , artificial intelligence , fuzzy number , function (biology) , 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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research