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GENUINE SETS, VARIOUS KINDS OF FUZZY SETS AND FUZZY ROUGH SETS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2003In this paper, deriving the type-m fuzzy sets, intuitionistic fuzzy sets, Φ-fuzzy sets, rough sets, fuzzy rough sets and rough fuzzy sets as particular genuine sets, and establishing their connections with genuine sets, it is demonstrated that the theory of genuine sets provides a powerful tool to model various different kinds of uncertainty in a ...
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Fuzzy Setting: Fuzziness of General Information
2012The aim of this paper is to give, on the fuzzy setting, a definition of fuzziness for the information without probability or fuzzy measure (general information).
VIVONA, Doretta, Maria Divari
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Fuzzy Sets and Systems, 2009
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Mila Stojakovic, Zoran Stojakovic
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Mila Stojakovic, Zoran Stojakovic
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IEEE Transactions on Fuzzy Systems, 2002
The objective of this paper is to investigate the innovative concept of complex fuzzy sets. The novelty of the complex fuzzy set lies in the range of values its membership function may attain. In contrast to a traditional fuzzy membership function, this range is not limited to [0, 1], but extended to the unit circle in the complex plane.
Daniel Ramot +3 more
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The objective of this paper is to investigate the innovative concept of complex fuzzy sets. The novelty of the complex fuzzy set lies in the range of values its membership function may attain. In contrast to a traditional fuzzy membership function, this range is not limited to [0, 1], but extended to the unit circle in the complex plane.
Daniel Ramot +3 more
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Information Sciences, 1996
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Mohua Banerjee, Sankar K. Pal
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Mohua Banerjee, Sankar K. Pal
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1997
The main aim of the chapter is somewhat terminological. We shall first fix terminology, and notation, and secondly mention some well known results used in the book.
Beloslav Riečan, Tibor Neubrunn
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The main aim of the chapter is somewhat terminological. We shall first fix terminology, and notation, and secondly mention some well known results used in the book.
Beloslav Riečan, Tibor Neubrunn
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2006 IEEE International Conference on Fuzzy Systems, 2006
In this paper we introduce a fuzzy implication. The proposed fuzzy implication does not belong in one of the well known three general classes of fuzzy implications (S-implications, R-implications and QL-implications). Also we give an extended model of this fuzzy implication in intuitionistic fuzzy set and/or interval-valued fuzzy sets.
Anestis G. Hatzimichailidis +2 more
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In this paper we introduce a fuzzy implication. The proposed fuzzy implication does not belong in one of the well known three general classes of fuzzy implications (S-implications, R-implications and QL-implications). Also we give an extended model of this fuzzy implication in intuitionistic fuzzy set and/or interval-valued fuzzy sets.
Anestis G. Hatzimichailidis +2 more
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On Convexity of Fuzzy Sets and Fuzzy Relations
Information Sciences, 1992A fuzzy set \(A\) on the vector space \(X\) (i.e. \(A\) is a mapping from \(X\) to the unit interval) is called convex if all \(\alpha\)-cuts of \(A\) are convex. The question that paper deals with is: For which operations applied to convex fuzzy sets or convex fuzzy relations is the resulting fuzzy set or relation again convex?
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2002
This paper presents a fuzzy set approach to the rule-based tagging. Both lexical and contextual phases have been shortly discussed to point the potential advantages of using an uncertain part-of-speech information. Obtained results are comparable with the results of other taggers, for example, Brill tagger.
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This paper presents a fuzzy set approach to the rule-based tagging. Both lexical and contextual phases have been shortly discussed to point the potential advantages of using an uncertain part-of-speech information. Obtained results are comparable with the results of other taggers, for example, Brill tagger.
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