由模糊度量引起的间性

IF 3.1 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Fuzzy Sets and Systems Pub Date : 2025-03-15 Epub Date: 2024-12-24 DOI:10.1016/j.fss.2024.109254

Zhenyu Jin , Conghua Yan

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引用次数: 0

摘要

受Zhang等人[28]，[38]，[39]的启发，我们的目标是进一步讨论 -间关系。在本文中，我们提供了用GV模糊度量来构造向量间性的方法。在考虑了关于强模糊度量的f -等价的描述之后，我们研究了f -e -间性的构造。讨论了-间性和- e间性的一些性质，并验证了[28]中的一些结论或条件。本文也是b[28]的补充。最后，研究了模糊化凸结构与横间关系之间的关系。

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⁎-betweenness induced by fuzzy metrics

Motivated by Zhang et al. [28], [38], [39], we aim to make a further discussion of ⁎-betweenness relations. In this paper, we provide the method to construct the ⁎-betweenness by GV fuzzy metrics. After considering the description of ⁎-equivalence with respect to strong fuzzy metrics, we investigate the construction of ⁎-E-betweenness. Some properties of ⁎-betweenness and ⁎-E-betweenness are discussed and some conclusions or conditions in [28] are verified. This paper is also a supplement of [28]. Finally, the relationship between fuzzifying convex structures and ⁎-betweenness relations has been studied.

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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.