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

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.

Automated summary

Intuitionistic fuzzy sets (IFSs) have advantages over fuzzy sets for describing imprecise information, but traditional theories fail to produce membership functions (MF) and nonmembership functions (NMF) for IFSs. This paper presents an alternative algorithm based on q-rung orthopair fuzzy set to generate these grading functions, making it convenient to model imprecise and vague information.