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Granular Computing

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24.03.2020 | Original Paper

Knowledge measure and entropy: a complementary concept in fuzzy theory

verfasst von: Vikas Arya, Satish Kumar

Erschienen in: Granular Computing | Ausgabe 3/2021

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Abstract

The knowledge measure can be considered as a dual measure of entropy for fuzzy sets. In the present work, a new entropy-based knowledge measure is proposed for FSs, which complies with the extended idea of De Luca and Termini axioms. Besides this, some of its major properties are also discussed. Comparison of the proposed measure with various existing fuzzy measures indicates that the proposed knowledge measure has a greater ability in discrimination of various FSs. Moreover, a fuzzy inaccuracy measure is introduced based on the proposed measure and investigated some properties. Considering the significance of integrated weights, a new multiple attribute decision-making (MADM) model is introduced under fuzzy set environment. The proposed knowledge measure is utilized to calculate the weights vector, when weights are partially known and other when weights are completely unknown. Finally, an example is employed to illustrate the effectiveness and consistency of the new MADM method.

Vorheriger Artikel Intuitionistic fuzzy time series functions approach for time series forecasting

Nächster Artikel Complex Pythagorean Dombi fuzzy graphs for decision making

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Titel: Knowledge measure and entropy: a complementary concept in fuzzy theory
verfasst von: Vikas Arya
Satish Kumar
Publikationsdatum: 24.03.2020
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 3/2021
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-020-00221-7

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