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模糊集中的无限非概率测度熵 被引量:2
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作者 杜凯 万晖 辛小龙 《西北大学学报(自然科学版)》 CAS CSCD 北大核心 2004年第3期263-266,共4页
目的 为得到模糊信息论的一些重要性质和定理。方法 以模糊熵的数学性质为基础,以Shannon熵为工具进行研究。结果 给出了无限非概率测度熵的定义及性质,并对无限非概率测度条件熵及模糊互信息给出了定义,进而研究了其性质并给出了相... 目的 为得到模糊信息论的一些重要性质和定理。方法 以模糊熵的数学性质为基础,以Shannon熵为工具进行研究。结果 给出了无限非概率测度熵的定义及性质,并对无限非概率测度条件熵及模糊互信息给出了定义,进而研究了其性质并给出了相关定理。结论 其结果深化和发展了模糊信息论的内容。 展开更多
关键词 模糊集 无限非概率测度熵 模糊互信息
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模糊事件的概率测度熵 被引量:3
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作者 王平 张国立 《华北电力大学学报(自然科学版)》 CAS 北大核心 2003年第1期100-102,共3页
通过对模糊事件及其概率测度的定义引出有限集上模糊子集熵的概念。运用模糊事件及概率测度的基本性质,给出了模糊熵的一些基本定理。
关键词 模糊事件 概率测度熵 模糊数学
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Topological r-entropy and measure-theoretic r-entropy of a continuous map 被引量:5
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作者 REN YunLi HE LianFai +1 位作者 LV JinFeng ZHENG GuoPing 《Science China Mathematics》 SCIE 2011年第6期1197-1205,共9页
In this paper,we introduce the concept of measure-theoretic r-entropy of a continuous map on a compact metric space,and get the results as follows:1.Measure-theoretic entropy is the limit of measure-theoretic r-entrop... In this paper,we introduce the concept of measure-theoretic r-entropy of a continuous map on a compact metric space,and get the results as follows:1.Measure-theoretic entropy is the limit of measure-theoretic r-entropy and topological entropy is the limit of topological r-entropy(r → 0);2.Topological r-entropy is more than or equal to the supremum of 4r-entropy in the sense of Feldman's definition,where the measure varies among all the ergodic Borel probability measures. 展开更多
关键词 topological r-entropy measure-theoretic r-entropy
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Robust hypothesis testing for asymmetric nominal densities under a relative entropy tolerance
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作者 Enbin Song Qingjiang Shi +1 位作者 Yunmin Zhu Jianxi Pan 《Science China Mathematics》 SCIE CSCD 2018年第10期1851-1880,共30页
In this paper, we address an open problem raised by Levy(2009) regarding the design of a binary minimax test without the symmetry assumption on the nominal conditional probability densities of observations. In the bin... In this paper, we address an open problem raised by Levy(2009) regarding the design of a binary minimax test without the symmetry assumption on the nominal conditional probability densities of observations. In the binary minimax test, the nominal likelihood ratio is a monotonically increasing function and the probability densities of the observations are located in neighborhoods characterized by placing a bound on the relative entropy between the actual and nominal densities. The general minimax testing problem at hand is an infinite-dimensional optimization problem, which is quite difficult to solve. In this paper, we prove that the complicated minimax testing problem can be substantially reduced to solve a nonlinear system of two equations having only two unknown variables, which provides an efficient numerical solution. 展开更多
关键词 Kullback-Leibler divergence robust hypothesis testing min-max problem least-favorable densities saddle point
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