Some Enhanced Distance Measuring Approaches Based on Pythagorean Fuzzy Information with Applications in Decision Making

Autor: Keke Wu, Paul Augustine Ejegwa, Yuming Feng, Idoko Charles Onyeke, Samuel Ebimobowei Johnny, Sesugh Ahemen
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Symmetry, Vol 14, Iss 12, p 2669 (2022)
Druh dokumentu: article
ISSN: 14122669
2073-8994
DOI: 10.3390/sym14122669
Popis: The construct of Pythagorean fuzzy distance measure (PFDM) is a competent measuring tool to curb incomplete information often encountered in decision making. PFDM possesses a wider scope of applications than distance measure under intuitionistic fuzzy information. Some Pythagorean fuzzy distance measure approaches (PFDMAs) have been developed and applied in decision making, albeit with some setbacks in terms of accuracy and precision. In this paper, some novel PFDMAs are developed with better accuracy and reliability rates compared to the already developed PFDMAs. In an effort to validate the novel PFDMAs, some of their properties are discussed in terms of theorems with proofs. In addition, some applications of the novel PFDMAs in problems of disease diagnosis and pattern recognition are discussed. Furthermore, we present comparative studies of the novel PFDMAs in conjunction to the existing PFDMAs to buttress the merit of the novel approaches in terms of consistency and precision. To end with, some new Pythagorean fuzzy similarity measuring approaches (PFDSAs) based on the novel PFDMAs are presented and applied to solve the problems of disease diagnosis and pattern recognition as well.
Databáze: Directory of Open Access Journals
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