基于Lukasiewicz蕴涵算子的Vague集相似度量

分析了Vague集存在未知度,表明了存在不确定性的原因。指出了现有Vague集相似度量方法存在的不足。在充分考虑了Vague集不确定性和对Lukasiewicz蕴涵算子进行研究后,提出了一个基于Lukasiewicz蕴涵算子的Vague集相似度量新方法;并证明该方法满足相似度量基本准则。通过与现有相似度量方法的比较,说明新的相似度量方法克服了现有相似度量方法的不足,考虑了未知度因素对相似度量的影响,能够有效合理地区分数据。

Similarity Measure Between Vague Sets Based On Lukasiewicz Implication Operator

Analyzes the reason that unknown degree of Vague sets indicates uncertainty,and points out some faults of the existing similarity measure between Vague sets.Considering the uncertainty of Vague sets and analyzing the relations of Lukasiewicz implication operator, , a new method for similarity measure between vague sets based on Lukasiewicz implication operator is proposed. The new similarity measure satisfy some basic rules. With comparison of the existing similarity measures, the new similarity measure overcomes the defects of the existing similarity measure methods. The new similarity measure fully concerns about the influence caused by unknown degree and can reasonably, effectively distinguish the data.