基于关联熵系数的粗糙Vague集相似性度量方法

粗糙Vague集是将粗糙集和Vague集理论相互融合以处理不确定性信息的一种理论工具。本文在深入研究Vague集及粗糙模糊集的关联熵、关联熵系数及集合相似性度量方法基础上，将关联熵和关联熵系数的概念引入到粗糙Vague集，并详细讨论了它们的主要性质，同时证明了关联熵系数满足粗糙Vague集相似度的定义，可用于粗糙Vague集的相似性度量。最后通过实例验证了粗糙Vague集的关联熵系数用于度量粗糙Vague集之间相似性程度的有效性，该理论为粗糙Vague集相似性度量提供了一种新方法。

Measurement method of the similarity of rough vague sets based on relative entropy coefficient

The rough Vague set is a theoretical tool that combines the theories of rough and Vague sets to deal with uncertain information. In this paper, we introduce the concept of relative entropy and its coefficient to a rough Vague set to investigate a method for measuring relative entropy, its coefficient, and the similarity of Vague and rough fuzzy sets. We also analyzed their main properties. We verified that the coefficient of the relative entropy has similarity with that of rough Vague sets, and that this coefficient can be used to measure the similarity of rough Vague sets. Finally, we conducted a case study to verify the effectiveness of using the relative entropy coefficient of a rough Vague set to determine the degree of similarity between rough Vague sets. This theory provides a new method for measuring the similarity of rough Vague sets.