关于自相似测度绝对连续的测度的点密度测度

本文研究了一类关于自相似测度绝对连续的概率测度的点密度测度的问题.利用迭代函数系,量子系数和H(o|¨)lder不等式,在自相似集满足强分离条件下,获得了此点密度测度,推广了自相似测度为Lebesgue测度的结果.     

通弦左边限制测度与双边限制测度

给出了通弦左边限制测度的定义及其等价性的刻画,应用反射布朗运动方法构造了左边限制测度,得到了双边限制测度和单边限制测度的关系.     

偏序集上的测度拓扑和全测度

在偏序集上引入测度拓扑和全测度概念,研究其性质以及与其它内蕴拓扑间的众多关系。主要结果有:连续偏序集的测度拓扑实际上是由其上的任一全测度所决定且可由它的定向完备化上的测度拓扑和全测度分别限制得到;当连续偏序集还是D om a in时,其上的测度拓扑与μ拓扑一致;连续偏序集有可数基当且仅当其上的测度拓扑是可分的;一个网如果测度收敛则存在最终上确界;任一ω连续偏序集上都存在全测度。     

区间值直观模糊集的相似测度和距离测度

对现有的模糊集和直观模糊集的相似测度和距离测度的公理化定义进行分析,并做出改进;然后提出区间值直观模糊集的相似测度和距离测度的公理化定义,并各引入它们的一种计算方法;最后给出区间值直观模糊集的相似测度和距离测度在模式识别中的一个应用实例.     

模糊熵,距离测度和散度测度

在模糊集的运算基础上,讨论如何用模糊熵定义模糊距离测度,并且研究了模糊距离测度和散度测度的关系。     

关于自保形测度与Lebesgue测度的等价性

本文我们研究了自保形测度与Lebesgu测度的关系,对Yuvla Peres等的结果进行了推广,证明了自相似测度要么是奇异的,要么关于Lebesgue测度 绝对连续的,并且若将Lebesgue测度限制在自相似测度的紧支撑上,则其关于非奇异的自相似测度是绝对连续的。     

正测度Cantor集与正测度Cantor函数

本文利用类比的方法,将Cantor集上定义的Cantor函数进行了推广.首先给出了正测度Cantor集及正测度Cantor函数的定义;然后通过严格的证明得到了正测度Cantor函数的一些性质,并给出了正测度Cantor函数的一些应用;最后通过实例说明,由于正测度Cantor函数构造的特殊性,可以用来作为一些命题的反例.     

测度空间上的gλ测度与条件gλ测度

本文在测度空间（Ｘ，μ）上引入了一类μ─密度函数ｆ所生成的ｇλ测度及条件ｇλ测度，并给出了与μ─密度函数ｆ相关的λ独立性的概念，得到了一些有关的结果     

网测度及应用

给出了n-进制网测度一个具体的定义,并证明了它的一些性质如:外测度、度量外测度.给出网测度的质量分布原理,证明了它与Hausdorff测度等价,并计算了直线上的三分Cantor集和Sierpinski地毯的网测度.     

可逆测度与拟可逆测度的等价性

本文讨论了一类速度函数的可逆与拟可逆测度间的关系,证明了在[2]的条件下,可逆测度与拟可逆测度是等价的。     

Star-Shaped deviations

We propose the Star-Shaped deviation measures in the same vein as Star-Shaped risk measures and Star-Shaped acceptability indexes. We characterize Star-Shaped deviation measures through Star-Shaped acceptance sets and as the minimum of a family of Convex deviation measures. We also expose an interplay between Star-Shaped risk measures and deviation measures.     

Generalized moment-independent importance measures based on Minkowski distance

Importance measures have been widely studied and applied in reliability and safety engineering. This paper presents a general formulation of moment-independent importance measures and several commonly discussed importance measures are unified based on Minkowski distance (MD). Moment-independent importance measures can be categorized into three classes of MD importance measures, i.e. probability density function based MD importance measure, cumulative distribution function based MD importance measure and quantile based MD importance measure. Some properties of the proposed MD importance measures are investigated. Several new importance measures are also derived as special cases of the generalized MD importance measures and illustrated with some case studies.     

Lattice measures and associated outer measures

In this paper, we consider lattice measures and introduce certain associated outer measures (not the usual induced outer measures), study their properties, and investigate the associated classes of measureable sets. We utilize some of these outer measures to characterize normality and investigate lattice separation properties; also, to extend the notion of regularity of measures to weak regularity of measures. We give applications of our results to specific topological lattices.     

Measures of Asymmetry Dual to Mean Minkowski Measures of Asymmetry for Convex Bo dies

We introduce a family of measures (functions) of asymmetry for convex bodies and discuss their properties. It turns out that this family of measures shares many nice properties with the mean Minkowski measures. As the mean Minkowski measures describe the symmetry of lower dimensional sections of a convex body, these new measures describe the symmetry of lower dimensional orthogonal projections.     

Isotopy-invariant topological measures on closed orientable surfaces of higher genus

Given a closed orientable surface Σ of genus at least two, we establish an affine isomorphism between the convex compact set of isotopy-invariant topological measures on Σ and the convex compact set of additive functions on the set of isotopy classes of certain subsurfaces of Σ. We then construct such additive functions, and thus isotopy-invariant topological measures, from probability measures on Σ together with some additional data. The map associating topological measures to probability measures is affine and continuous. Certain Dirac measures map to simple topological measures, while the topological measures due to Py and Rosenberg arise from the normalized Euler characteristic.     

模糊软集的相似测度和距离测度(英文)

In this paper we introduce several new similarity measures and distance measures between fuzzy soft sets, these measures are examined based on the set-theoretic approach and the matching function. Comparative studies of these measures are derived. By introducing two general formulas, we propose a new method to define the similarity measures and the distance measures between two fuzzy soft sets with different parameter sets.     

Orlicz–Young Structures of Young Measures

The paper examines the integration of Young functions applied to Young measures and identifies Orlicz-like structures in the space of Young measures. In particular, a convergence intermediate between the weak convergence of measures and the variational norm is determined; it serves in the completion of the Orlicz space of functions when interpreted as degenerate Young measures. Partial linear operations are defined on Young measures with respect to which the linear operations in the Orlicz space of functions are continuously embedded in the space of Young measures. This leads to a definition of convexity-type structures in the space of Young measures via a limiting procedure. These structures enable applications of Young functions arguments to Young measures. Applications to optimal control and to well posedness of minimization in function spaces with respect to convex functions are provided.     

Products of Capacities Derived from Additive Measures

One of the unanswered questions in non-additive measure theory is how to define product of non-additive measures. Most of the approaches that have already been presented only work for discrete measures. In this paper a new approach is presented for not necessarily discrete non-additive measures that are in a certain relation with additive measures, usually this means that they are somehow derived from the additive measures.     

Subdifferential representations of risk measures

Measures of risk appear in two categories: Risk capital measures serve to determine the necessary amount of risk capital in order to avoid ruin if the outcomes of an economic activity are uncertain and their negative values may be interpreted as acceptability measures (safety measures). Pure risk measures (risk deviation measures) are natural generalizations of the standard deviation. While pure risk measures are typically convex, acceptability measures are typically concave. In both cases, the convexity (concavity) implies under mild conditions the existence of subgradients (supergradients). The present paper investigates the relation between the subgradient (supergradient) representation and the properties of the corresponding risk measures. In particular, we show how monotonicity properties are reflected by the subgradient representation. Once the subgradient (supergradient) representation has been established, it is extremely easy to derive these monotonicity properties. We give a list of Examples.     

Multifractal analysis of a measure of multifractal exact dimension

We focus on the multifractal generalization of the centered Hausdorff measure and dimension. We analyze the correlation among different approaches to the definition of the multifractal exact dimension of locally finite and Borel regular measures on the basis of fractal analysis of essential supports of these measures. Using characteristic multifractal measures, we carry out the multifractal analysis of singular probability measures and prove theorems on the structural representation of these measures.