典型集合和度量的一般分形维数

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Rim Achour, Bilel Selmi
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引用次数: 0

摘要

将 (Y,ρ) 视为完全度量空间,S 视为 Y 上的概率玻尔量空间。设 dim‾BΨ,Φ(E) 为集合 E⊂Y 的一般上箱维度。我们首先证明,在贝雷范畴的意义上,典型紧凑集的一般包装维度至少是 inf{dim‾BΨ,Φ(B(x,r))|x∈Y,r>0} 其中 B(x,r) 是 Y 中以 x 为中心、以 r>0 为半径的闭球。接下来,我们会得到一些关于贝雷范畴意义上的典型度量的一般上下盒维的估计值。最后,我们证明,如果 S 具有弱拓扑,并且在某些假设条件下,那么具有一般上下相关维数为零的度量集合是残差的。此外,典型度量的一般上相关维度(在贝雷范畴的意义上)是通过 Y 的一般局部下熵维度和上熵维度近似得到的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
General fractal dimensions of typical sets and measures

Consider (Y,ρ) as a complete metric space and S as the space of probability Borel measures on Y. Let dimBΨ,Φ(E) be the general upper box dimension of the set EY. We begin by proving that the general packing dimension of the typical compact set, in the sense of the Baire category, is at least inf{dimBΨ,Φ(B(x,r))|xY,r>0} where B(x,r) is the closed ball in Y with center at x and radii r>0. Next, we obtain some estimates of the general upper and lower box dimensions of typical measures in the sense of the Baire category. Finally, we demonstrate that if S is equipped with the weak topology and under some assumptions then the set of measures possessing the general upper and lower correlation dimension zero are residual. Furthermore, the general upper correlation dimension of typical measures (in the sense of the Baire category) is approximated through the general local lower and upper entropy dimensions of Y.

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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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