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Erschienen in: Neural Computing and Applications 6/2017

07.01.2016 | Review

Correlation coefficient of single-valued neutrosophic hesitant fuzzy sets and its applications in decision making

verfasst von: Rıdvan Şahin, Peide Liu

Erschienen in: Neural Computing and Applications | Ausgabe 6/2017

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Abstract

As a combination of the hesitant fuzzy set (HFS) and the single-valued neutrosophic set (SVNS), the single-valued neutrosophic hesitant fuzzy set (SVNHFS) is an important concept to handle uncertain and vague information existing in real life, which consists of three membership functions including hesitancy, as the truth-hesitancy membership function, the indeterminacy-hesitancy membership function and the falsity-hesitancy membership function, and encompasses the fuzzy set, intuitionistic fuzzy set (IFS), HFS, dual hesitant fuzzy set (DHFS) and SVNS. Correlation and correlation coefficient have been applied widely in many research domains and practical fields. This paper, motivated by the idea of correlation coefficients derived for HFSs, IFSs, DHFSs and SVNSs, focuses on the correlation and correlation coefficient of SVNHFSs and investigates their some basic properties in detail. By using the weighted correlation coefficient information between each alternative and the optimal alternative, a decision-making method is established to handling the single-valued neutrosophic hesitant fuzzy information. Finally, an effective example is used to demonstrate the validity and applicability of the proposed approach in decision making, and the relationship between the each existing method and the developed method is given as a comparison study.

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Metadaten
Titel
Correlation coefficient of single-valued neutrosophic hesitant fuzzy sets and its applications in decision making
verfasst von
Rıdvan Şahin
Peide Liu
Publikationsdatum
07.01.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2163-x

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