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Knowledge measure for the q‐rung orthopair fuzzy sets
Author(s) -
Khan Muhammad Jabir,
Kumam Poom,
Shutaywi Meshal
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.22313
Subject(s) - measure (data warehouse) , mathematics , fuzzy set , membership function , fuzzy logic , computer science , entropy (arrow of time) , generalization , type 2 fuzzy sets and systems , artificial intelligence , data mining , mathematical analysis , physics , quantum mechanics
The q‐rung orthopair fuzzy set (qROFS) defined by Yager is a generalization of Atanassov intuitionistic fuzzy set and Pythagorean fuzzy sets. In this paper, we define the knowledge measure for qROFS by using the tangent inverse function. This is the first approach to quantify the knowledge associated with qROFS. The membership and nonmembership functions as well as the hesitancy margin are used to define the knowledge measure which makes it capable of considering both knowledge and fuzziness. The entropy measure which is the dual of the knowledge measure is also defined. The properties of the proposed knowledge measure with graphical explanations are discussed. An application of the proposed knowledge measure in multiattribute group decision making problem under confidence level approach is given.