普遍积分的关系代数方法

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Michael Winter
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引用次数: 0

摘要

克莱门特等人在一系列论文中研究了离散积分(如乔克特积分和杉野积分)及其公理化。作为研究的一部分,他们证明了普遍积分是基于半公式的,并提供了基于给定半公式的积分运算的下限和上限。这些实值积分和单位区间值积分可被视为模糊集背景下的适当聚合工具。本文的目的是将这种方法推广到所谓的 L-模糊集合和关系,即使用任意海廷代数 L 代替单位区间作为成员度的模糊集合和关系。此外,我们在箭头范畴内提出了这一理论,也就是说,我们从具体的集合和关系中抽象出来,在一个合适的代数框架内工作。本文还表明,前人的研究成果无需提及实数和具体的度量(如勒比斯克度量和诱导可测空间)即可证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A relation algebraic approach to universal integrals

In a series of papers Klement et al. investigated discrete integrals such as the Choquet and Sugeno integral and their axiomatization. As part of their study they showed that universal integrals are based on semicopulas, and they provided lower and upper bounds of the integral operations based on a given semicopula. These real-valued resp. unit interval valued integrals can be considered as proper aggregation tool in the context of fuzzy sets. The aim of the current paper is to generalize this approach to so-called L-fuzzy sets and relations, i.e., fuzzy sets and relations that use an arbitrary Heyting algebra L as membership degree instead of the unit interval. Furthermore, we present the theory within arrow categories, i.e., we abstract from concrete sets and relations and work within a suitable algebraic framework. The current paper also shows that the results of the previous work can be proven without referring to the real numbers and specific measures such as the Lebesque measure and the induced measurable spaces.

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来源期刊
Fuzzy Sets and Systems
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.
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