基于新的区间直觉模糊集相似性测度的模式识别

有关区间值直觉模糊数（集）的相似性研究较少，并且现有的方法在处理实际问题时效果较差。针对这个问题，提出了区间值直觉模糊数（集）相似性测度的新方法，包含了隶属度，非隶属度，犹豫度，以及后者对前两者的影响，将隶属度，非隶属度，犹豫度的相似度表示成三元组的形式，用TOPSIS的思想处理该三元组，得出一种新的有效的相似性度，证明其合理性。将其应用到模式识别实例中，验证其有效性。

Method of pattern recognition based on new similarity measure of interval-valued intuitionistic fuzzy set

There are few researches related to similarity measure of the interval-valued intuitionistic fuzzy set, and these existing methods are inefficient to handle practical problems. To solve this problem, this paper proposes a new method in terms of similarity measure of the interval-valued intuitionistic fuzzy set, involving the degree of membership, the degree of non-membership and the degree of hesitation, as well as the third one’s effect on the anterior both. The developed method expresses the similarity measures of the degree of membership, the degree of non-membership and the degree of hesitation in the form of triple. This paper introduces a new approach of similarity measure handling the triple with the idea of TOPSIS and proves its feasibleness. An illustrative example about pattern recognition is given to verify the efficiency of the developed approach.