Abstract: It is difficult to describe the complex influence of uncertain information on similarity measurement of Vague value（set） which is neglected by the most existing measurements. Therefore, based on the existing main methods, a new similarity measure method of Vague value（set） is proposed and its properties are systematically analyzed considering the influence of uncertain information on similarity measurement of Vague value（set） in the quadratic voting model. This method has better similarity discrimination ability than the existing ones. The new similarity measure of Vague value（set） is applied to multi-criteria fuzzy decision-making. The better scheme is obtained by comparing the similarity under the criterion of the candidate scheme with ideal scheme’s. The validity and feasibility of this method are verified by an example.

Key words: similarity measure, true , false membership function, Vague set

摘要： 不确定信息对Vague值（集）相似性度量的影响非常复杂、难以刻画，现有的度量方法大多都忽略了该因素。为此，在已有主要研究方法的基础上尝试以二次投票模型为背景考虑了不确定信息对Vague值（集）相似性的影响，提出一种新的Vague值（集）相似性度量方法，并对其性质进行了较系统的分析。通过与现有主要方法的比较，表明该方法具有较好的相似度区分能力。将新的Vague值（集）相似度量用于多准则模糊决策中，考虑候选方案与理想方案在相应准则下相似度的大小得出最佳方案。并通过实例分析验证了该方法的有效性和可行性。

关键词: 相似性度量, 真, 假隶属函数, Vague集