نتایج جستجو برای: fuzzy upper e limit set

تعداد نتایج: 2025052  

Journal: :Int. J. Fuzzy Logic and Intelligent Systems 2014
Yong Chan Kim

In this paper, we investigate the properties of L-lower approximation operators as a generalization of fuzzy rough set in complete residuated lattices. We study relations lower (upper, join meet, meet join) approximation operators and Alexandrov L-topologies. Moreover, we give their examples as approximation operators induced by various L-fuzzy relations.

The backing of the fuzzy ideal is normal ideal in some ring and in same time there fuzzy set whose is not fuzzy ideal and it backing set is ideal, i.e., it crisp is normal ideal. Consequently, in this paper we constructing a fuzziness function which defined on fuzzy sets and assigns membership grade for every fuzzy set whose it backing set are crisp ideal. Now, Let  be collection of all fuzzy s...

2017
A. Nagoor Gani Prasanna Devi

In this paper, the cobondage set and cobondage number bC(G) of a fuzzy graph G are defined. The upper bound of a fuzzy graph with p nodes is given as min(p − (1 + S(G)), S(G) + 1). The exact values of bC(G) for some standard fuzzy graphs are found. Some results on bC(G) are also discussed. A numerical example is also illustrated. AMS subject classification: 03E72, 05C40, 05C72.

2015
A. Nagoor Gani K. Prasanna Devi Muhammad Akram A. N. Gani K. P. Devi M. Akram A. Somasundram

In this paper, bondage and non-bondage set of a fuzzy graph are discussed. The bondage number b(G) and non-bondage number bn(G) of a fuzzy graph G are defined. The upper bound for both b(G) and bn(G) are given. Also some results on b(G) and bn(G) are discussed. The exact values of b(G) and bn(G) are determined for several classes of fuzzy graphs. AMS Subject Classification: 03E72, 05C40, 05C72

Journal: :Fuzzy Sets and Systems 2011
Oleh R. Nykyforchyn Dusan Repovs

Crisp and L-fuzzy ambiguous representations of closed subsets of one space by closed subsets of another space are introduced. It is shown that, for each pair of compact Hausdorff spaces, the set of (crisp or L-fuzzy) ambiguous representations is a lattice and a compact Hausdorff Lawson upper semilattice. The categories of ambiguous and L-ambiguous representations are defined and investigated.

2015

A search for a charged Higgs boson is performed with a data sample corresponding to an integrated luminosity of 19.7± 0.5 fb−1 collected with the CMS detector in proton-proton collisions at √ s = 8 TeV. The charged Higgs boson is searched for in top quark decays for mH± < mt − mb, and in the direct production pp→ t(b)H± for mH± > mt − mb. The H± → τ±ντ and H± → tb decay modes in the final state...

Journal: :Fuzzy Sets and Systems 2001
John N. Mordeson

We use covers of the universal set to de-ne approximation operators on the power set of the given set. In Section 1, we determine basic properties of the upper approximation operator and show how it can be used to give algebraic structural properties of certain subsets. We de-ne a particular cover on the set of ideals of a commutative ring with identity in such a way that both the concepts of t...

2002
Peter Austing Thordur Jonsson

We study scalar solitons on the fuzzy sphere at arbitrary radius and noncommutativity. We prove that no solitons exist if the radius is below a certain value. Solitons do exist for radii above a critical value which depends on the noncommutativity parameter. We construct a family of soliton solutions which are stable and which converge to solitons on the Moyal plane in an appropriate limit. The...

2008
Chris Cornelis Martine De Cock Anna Maria Radzikowska

Fuzzy sets and rough sets address two important, and mutually orthogonal, characteristics of imperfect data and knowledge: while the former allow that objects belong to a set or relation to a given degree, the latter provide approximations of concepts in the presence of incomplete information. In this chapter, we demonstrate how these notions can be combined into a hybrid theory that is able to...

Journal: :Theor. Comput. Sci. 2011
Richard Jensen Chris Cornelis

In this paper, we propose a nearest neighbour algorithm that uses the lower and upper approximations from fuzzy rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other nearest neighbour approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction ...

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