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。     

关于模糊值函数序列的C-I平均收敛

在一般模糊测度空间上,利用模糊值Choquet积分定义首次给出了模糊值函数列的C-I平均收敛、C-I平均基本等概念,并针对μ-可积模糊值函数列进一步研究了它的C-I平均收敛与依模糊测度收敛、C-I平均基本与依模糊测度基本之间的蕴涵关系.     

模糊数测度空间上模糊值函数的模糊值积分的Fubini定理

建立卡氏积空间 (X×Y,S× T)上的乘积区间值测度和乘积模糊值测度 ,证明模糊值测度空间上模糊值函数的模糊值积分的 Fubini定理。     

模糊熵与距离测度的相互诱导及其应用

模糊信息论就是利用模糊数学这一工具来研究带有模糊不确定性的信息的．模糊熵和距离测度是模糊信息论中两个重要的度量方法．本文主要讨论模糊熵和距离测度之间的相互关系,由此得到几个由模糊熵诱导的距离测度公式和几个由距离测度诱导出的模糊熵公式,说明了模糊熵和距离测度是可以相互诱导的．最后,举例说明距离测度公式在模式识别中的应用．     

一类模糊测度及其扩展定理

本文在模系(I,∨,∧,c)中讨论模糊单调列的收敛性质,引进由模糊代数所产生的模糊σ代数和模糊单调类,且证明二者是相等的。然后定义模糊外测度与模糊内测度,并证明其与经典的外测度和内测度有许多类似性质,最后引进正规强连续模糊测度概念,且论证一定义在模糊代数上的正规强连续模糊测度可唯一地扩展到由此模糊代数所产生的模糊σ代数上。     

Banach空间上集值测度与模糊数测度的扩张

在Banach空间上,给出集值测度的扩张定理并借助集测度的扩张给出了模糊数测度的扩张定理。     

模糊复测度空间上可测函数列几种收敛性之间的关系

作为经典复测度和模糊测度的推广,研究模糊复测度及模糊复测度空间上可测函数列几种收敛性之间的关系.在模糊复测度空间上得到了Egoroff定理、Lebesgue定理和Riesz定理等重要结果.为模糊复分析的深入研究打下一定基础.     

关于Sugeno测度的截尾方法

研究了经典的截尾方法在Sugeno测度空间上的推广这一问题。Sugeno测度是一种模糊测度,它的性质得到进一步的讨论。然后我们在模糊环境下给出了截尾方法的证明,得到了关于Sugeno测度的截尾方法。这一工作推广了截尾方法的研究范围和应用领域。     

加法与乘法下的模糊熵

由于模糊集关于普通加法与乘法运算构成的代数系统是分配格、布尔代数,这就使得模糊集的应用受到一定的限制,为了扩展模糊集的应用,本文引进了模糊集的加法与乘法运算,接着研究了模糊集关于加法与乘法的运算性质以及模糊熵,距离测度,相似性测度在集合关于加法运算,乘法运算下的一些性质,并且对加法与乘法下的模糊熵与普通模糊熵作了对比.     

多分类器融合系统中模糊测度的确定

有限事例集上,choquet模糊积分的计算可以转化成模糊测度的线性组合,故可以使用标准的优化技术来确定模糊测度。本文使用线性规划来确定模糊测度,数据库上的仿真实验表明,线性规划确定的模糊测度的系统融合精度比多数投票法和加权平均法的分类精度要高。     

Fuzzy measure of fuzzy events defined by fuzzy integrals

We define the concept of fuzzy measure of a fuzzy event by using a general form of fuzzy integral proposed by Murofushi, called fuzzy t-conorm integral, encompassing previous definitions. Zadeh defined the probability measure of a fuzzy event, and later the possibility measure of fuzzy event. Using a duality property of fuzzy t-conorm integral, we propose a general definition of fuzzy measure of fuzzy events, which is compatible with previous definitions of Zadeh, and possesses all properties of a fuzzy measure, in particular the duality property. Using our definition, we examine the case of decomposable measures and belief functions. A comparison with previous works is provided.     

Correspondence between fuzzy measures and classical measures

In this paper we study the question whether, given a fuzzy measure (as defined in [3] and [4]). there exists a classical measure such that the fuzzy measure of a measurable fuzzy set μ equals the classical measure of the area below the membership function of μ. The results are that in the case of finite additivity there is a one-to-one correspondence between classical measures and fuzzy measures, whereas in the case of countable additivity this result only holds for generated fuzzy σ-algebras. Finally, some connections of that problem with the existence of an extension of a fuzzy measure defined on an arbitrary fuzzy σ-algebra σ to the generated fuzzy σ-algebra $\text{σ}$ are discussed.     

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.     

基于区间数度量的区间值模糊集合的归一化距离、相似度、模糊度和包含度的关系研究

区间值模糊集合的距离、相似度、模糊度和包含度及其关系研究是区间值模糊集合的一个研究热点.考虑到区间值模糊集合所表示信息的丰富性,本文使用区间数而非实数来刻画区间值模糊集合的距离,首先给出基于区间数度量的区间值模糊集合的归一化距离的公理化定义,然后通过五个定理详细研究了基于公理化定义的区间值模糊集合的归一化距离、相似度、模糊度和包含度之间的相互转换关系,最后,给出了若干公式来计算基于区间数度量的区间值模糊集合的相似度、模糊度和包含度.这些结论,一方面丰富了区间值模糊集合的信息测度(距离、相似度、模糊度和包含度)的内容,另一方面也为区间值模糊集合的近似推理、决策分析、模式识别等领域的应用提供了新方法和新理论.     

A theoretical development on a fuzzy distance measure for fuzzy numbers

The objective of this paper is to introduce a fuzzy distance measure for generalized fuzzy numbers (GFN). It computes the fuzzy distance between two generalized fuzzy numbers and also LR-type fuzzy numbers. The metric properties of the proposed measure are also studied. Some numerical examples have been considered here for applying the proposed fuzzy distance measure and the results are compared.     

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.     

A generalized Borgonovo's importance measure for fuzzy input uncertainty

In this work, the way in which fuzzy uncertainty in a model's output is apportioned to fuzzy uncertainty in model inputs is studied through a sensitivity analysis. Here, an optimization technique is employed to obtain the membership functions of the fuzzy structural response, for which a global sensitivity indicator is introduced to measure the influence of fuzzy input uncertainty on fuzzy output uncertainty. The global sensitivity indicator is the important measure of the fuzzy input uncertainty, which extends Borgonovo's measure. In this study, the mathematical properties of the important measure of the fuzzy input uncertainty are discussed and proved. The results of numerical examples and engineering examples show that the proposed importance measure can effectively describe the effect of fuzzy input uncertainty on fuzzy structural response. When the sensitivity indicator is larger, the basic fuzzy-valued variable becomes more important. The sensitivity indicators of the fuzzy structural response can give an essential importance sequence of all the basic fuzzy-valued variables and identify key contributing fuzzy-valued variables. The sensitivity indicators can provide the availability guidance to reduce the number of basic variables and optimize the fuzzy response model.     

Fuzzy测度的伪自连续性

为了克服ＳｕｇｅｎｏＦｕｚｚｙ测度的局限性，从而在更一般的情况下研究ＳｕｇｅｎｏＦｕｚｚｙ测度和Ｆｕｚｚｙ积分，王震源在文［１］中首次引进集函数的“自连续性、伪自连续性”等一系列重要概念，孙清河在文［３］中解决了文［１］中提出的“上自连续性”与“下自连续性”的等价问题，而本文则解决了文［１］中提出的“伪上自连续性”与“伪下自连续性”的等价问题，并得到有关伪自连续定义的一系列等价命题。