Rougness, rough entropy, fuzziness, and fuzzy entropy are major methods for measuring the uncertainty of rough sets.
粗糙集的不确定性度量方法，目前主要包括粗糙集的粗糙度、粗糙熵、模糊度和模糊熵。
The image threshold segmentation algorithm based on the Particle Swarm Optimization (PSO) combined with the rough entropy based on boundary region is presented.
提出一种基于微粒群优化(PSO)的边界区域粗糙熵的阈值图像分割算法。
The concept of rough entropy is proposed. The monotony between the uncertainty of knowledge in the rough set theory and its corresponding rough entropy is proved.
提出了粗糙信息熵的概念，证明了粗糙集理论中知识不确定性与其所对应的粗糙信息熵之间的单调关系。
Moreover, the modified rough entropy and the fuzzy entropy based on equivalence relation is extended to the generalized modified rough entropy and the generalized fuzzy entropy.
并分别将基于不可分辨关系下的修正粗糙熵和模糊熵拓展到基于一般二元关系下的广义修正粗糙熵和广义模糊熵。
The analysis and real example show that the modified rough entropy method for measuring uncertainty in rough sets is more reasonable and more accurate than the classical methods.
通过分析和实例可以看出，所提出的修正粗糙熵方法可以用来更合理、更精确地测量粗糙集中的不确定性。
The rough entropy of the uncertainty of ordinary set and fuzzy set, and the monotonous relation between the uncertainty of these two kinds of set and their corresponding rough entropy, are discussed.
并研究了与普通集合和模糊集合的不确定性相对应的粗糙信息熵，以及这两种集合的不确定性与其对应的粗糙信息熵之间的单调关系。
The rough entropy of the uncertainty of ordinary set and fuzzy set, and the monotonous relation between the uncertainty of these two kinds of set and their corresponding rough entropy, are discussed.
并研究了与普通集合和模糊集合的不确定性相对应的粗糙信息熵，以及这两种集合的不确定性与其对应的粗糙信息熵之间的单调关系。
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