A New Method for Fuzzy Ranking Based on Possibility and Necessity Measures | Zendy

Mohammad Sadeghi Moghadam | Zendy; Tooraj Karimi | Zendy; Mohammad Bagher Menhaj | Zendy; Somaye Rahimi | Zendy

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A New Method for Fuzzy Ranking Based on Possibility and Necessity Measures

Author(s) -

Mohammad Sadeghi Moghadam,

Tooraj Karimi,

Mohammad Bagher Menhaj,

Somaye Rahimi

Publication year - 2020

Publication title -

international journal of computers and communications

Language(s) - English

Resource type - Journals

ISSN - 2074-1294

DOI - 10.46300/91013.2020.14.4

Subject(s) - rank (graph theory) , closeness , ranking (information retrieval) , fuzzy logic , fuzzy number , data mining , mathematics , computer science , interval (graph theory) , algorithm , fuzzy set , artificial intelligence , combinatorics , mathematical analysis

In this paper, a new method to rank fuzzy numbers is presented. The proposed method based on Possibility and Necessity Measures is called PNM. According to possibility and necessity measures, eight indexes are calculated to extract four rules to rank fuzzy numbers. Also a method to evaluate each rule validation especially when rules’ outcomes yield conflict conclusions is presented. To test PNM performance, some controversial triangular fuzzy numbers are considered. Additionally, four extracted rules are compared with each other and fully analyzed. Furthermore, PNM is compared with other recently proposed methods. Results confirm that PNM is capable to rank a variety of fuzzy numbers and their images with any selected bandwidths, interval and any degree of closeness

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