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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2019

23.05.2017 | Original Article

Using single axioms to characterize (S, T)-intuitionistic fuzzy rough approximation operators

verfasst von: Wei-Zhi Wu, Ming-Wen Shao, Xia Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2019

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Abstract

In this paper axiomatic characterizations of relation-based intuitionistic fuzzy rough approximation operators determined by an intuitionistic fuzzy triangular norm T and its dual intuitionistic fuzzy triangular conorm S on \([0, 1]\times [0, 1]\) are proposed. The constructive definitions and properties of S-lower and T-upper intuitionistic fuzzy rough approximation operators are first introduced. Operator-oriented characterizations of (S, T)-intuitionistic fuzzy rough approximation operators are then explored. Different sets of independent axioms for characterizing the essential properties of (S, T)-intuitionistic fuzzy rough approximation operators generated by various intuitionistic fuzzy relations are presented. Finally, it is examined that these sets of axioms can all be replaced by single axioms.
Vorheriger Artikel Reconstruction of sparse signals via neurodynamic optimization
Nächster Artikel Multi-kernel SVM based depression recognition using social media data

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Metadaten
Titel
Using single axioms to characterize (S, T)-intuitionistic fuzzy rough approximation operators
verfasst von
Wei-Zhi Wu
Ming-Wen Shao
Xia Wang
Publikationsdatum
23.05.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0696-2

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