非可加测度代数的一致结构和拓扑群性质

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Aoi Honda , Ryoji Fukuda , Yoshiaki Okazaki
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引用次数: 0

摘要

本文提出了一种分析具有非加性测度的可测空间的一致结构和拓扑群性质的新框架。与传统的依赖于可加性的方法不同,定义了新的概念,均匀性,k -均匀性和次k -均匀性,以建立非可加性测度代数的伪度量和拓扑群结构。结果表明,这些新的均匀性条件允许对非加性测度进行更一般的处理,弥合了经典测度理论与模糊测度空间之间的差距,重点关注代数a∧P(X)上定义的拟单调测度μ。空间(A,μ)是对称差分运算下的一个阿贝尔群。我们定义了μ的均匀性、k -均匀性和次k -均匀性的概念,建立了(A,μ)成为拓扑群的条件。通过构造(a,μ)上的一致结构和伪度量,我们证明了如果μ是k一致的,它通过幂变换与次加性测度密切相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uniform structure and topological group properties of non-additive measure algebras
This paper introduces a novel framework for analyzing the uniform structure and topological group properties of measurable spaces with non-additive measures. Unlike traditional approaches that rely on additivity, new concepts, uniformness, K-uniformness, and infra K-uniformness, are defined to establish a pseudo-metric and a topological group structure for non-additive measure algebras. The results demonstrate that these new uniformity conditions allow for a more general treatment of non-additive measures, bridging the gap between classical measure theory and fuzzy measure spaces, focusing on a quasi-monotone measure μ defined on an algebra AP(X). The space (A,μ) is an abelian group under the symmetric difference operation. We define the concepts of uniformness, K-uniformness, and infra K-uniformness for μ, establishing conditions under which (A,μ) becomes a topological group. By constructing a uniform structure and a pseudo-metric on (A,μ), we demonstrate that if μ is K-uniform, it relates closely to subadditive measures through power transformations.
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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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