基于包含度的直觉模糊相似度量推理方法

Intuitionistic fuzzy similarity measures reasoning method based on inclusion degrees

针对现有直觉模糊相似度量大都基于距离测度的现状,提出一种基于包含度的直觉模糊相似度量推理方法。所提方法利用模糊蕴涵算子及集合基数建立一系列直觉模糊包含度函数,并对基于包含度的强相似度量进行定义,给出满足强相似度量的若干性质,揭示了包含度与相似度之间的关系,进而将基于包含度的直觉模糊相似度量引入直觉模糊推理,提出了基于包含度的直觉模糊相似度量推理方法。以现有的10种直觉模糊相似度量方法与所提方法进行比较,并通过典型的实验数据分别展示了基于包含度的直觉模糊相似度量方法的优越性与较强的区分能力,实验目标样本的推理结果验证了所提方法具有较高的推理精度。

As to the present situation that most of similarity measures of intuitionistic fuzzy sets are based on measuring the distance, a method of intuitionistic fuzzy similarity measures reasoning based on inclusion de grees is proposed. Based on fuzzy implication operators and cardinal numbers, a series of intuitionistic fuzzy in clusion degrees sets is set up, and by defining strong similarity measures based on inclusion degrees, some prop erties related to strong similarity measures are put forward, and the relationship between inclusion degrees and similarity is revealed. Accordingly, a method of intuitionistic fuzzy similarity measures reasoning based on inclu sion degrees is proposed due to introducing intuitionistic fuzzy similarity measures into intuitionistic fuzzy rea soning. The superiority and discriminatory power of methods of intuitionistic fuzzy similarity measures reason ing based on inclusion degrees are presented compored with 10 methods and typical experimental data. And the reasoning results of experimental target samples prove that the advantage of the proposed method is accurate for calculation.