Roughness in formal concept analysis via multilattices

IF 3.2 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2024-11-19 DOI:10.1016/j.fss.2024.109179

G. Nguepy Dongmo , B.B. Koguep Njionou , L. Kwuida , M. Onabid

引用次数: 0

Abstract

Multilattices are very suitable to deal with fuzziness in non-deterministic environments. In this paper, we propose an extension of the work of Shao et al. by replacing a residuated lattice with a multilattice, as the algebraic structure of truth values in the set approximations within fuzzy formal concept analysis. We propose new fuzzy concept multilattices derived from adjoint pairs. In addition, we define two pairs of rough fuzzy set approximations in fuzzy formal contexts via multilattice, and we establish their characterizations.

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通过多网格进行形式概念分析的粗糙度

多网格非常适合处理非确定性环境中的模糊问题。在本文中，我们提出了对 Shao 等人工作的扩展，用多网格代替残差网格，作为模糊形式概念分析中集合近似中真值的代数结构。我们提出了由邻接对派生的新模糊概念多网格。此外，我们通过多网格定义了模糊形式语境中的两对粗糙模糊集近似，并确定了它们的特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Fuzzy Sets and Systems 数学-计算机：理论方法

CiteScore

6.50

自引率

17.90%

发文量

321

审稿时长

6.1 months

期刊介绍： Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.