نتایج جستجو برای: m fuzzifying convex structures

تعداد نتایج: 1019623  

O. R. Sayed R. A. Borzooei,

In this paper, based in the L ukasiewicz logic, the definition offuzzifying soft neighborhood structure and fuzzifying soft continuity areintroduced. Also, the fuzzifying soft proximity spaces which are ageneralizations of the classical soft proximity spaces are given. Severaltheorems on classical soft proximities are special cases of the theorems weprove in this paper.

The aim of this paper is to study the categorical relations betweenmatroids, Goetschel-Voxman’s fuzzy matroids and Shi’s fuzzifying matroids.It is shown that the category of fuzzifying matroids is isomorphic to that ofclosed fuzzy matroids and the latter is concretely coreflective in the categoryof fuzzy matroids. The category of matroids can be embedded in that offuzzifying matroids as a simul...

Journal: :Journal of Differential Geometry 2006

Journal: :Pacific Journal of Mathematics 1970

Journal: :J. Symb. Log. 2005
Marcus Tressl

Contents 1. Introduction. 2. Heirs. 3. The invariance group of a cut. 4. Review of T-convex valuation rings. 5. The invariance valuation ring of a cut. 6. A method for producing cuts with prescribed signature. 7. Existentially closed extensions. 1. Introduction. Let M be a totally ordered set. A (Dedekind) cut p of M is a couple (p L , p R) of subsets p L , p R of M such that p L ∪ p R = M and ...

2012
Frédéric Blanchard Philippe Vautrot Herman Akdag Michel Herbin

This paper presents an original definition of data representativeness. The representativeness of each datum in a dataset is a meaningful notion quantified by a degree computed by aggregating fuzzy subsets. These fuzzy subsets are obtained by fuzzifying data in a robust way. We illustrate the usefulness of the representativeness by presenting applications for statistical location estimation,and ...

2011
Ali F. Jameel Amir Sadeghi A. F. Jameel A. Sadeghi

In this paper, the fuzzy nonlinear programming problem is discussed. In order to obtain more accurate solution, the properties of fuzzy set and fuzzy number with linear membership function and fuzzy maximum decision maker is utilized to fuzzifying the crisp problem . An example is provided to show the effectiveness of the proposed method.

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