直觉模糊集间相似度量及其在方案优选中的应用

Similarity measure between intuitionistic fuzzy sets and its application to scheme optimum seeking

针对现有直觉模糊集(数)间相似度量方法大都未考虑犹豫度,其计算方法只适用于一些特定场合的问题,基于直觉模糊集的定义,提出了一种新的相似度量方法。新的相似度计算公式由隶属度、非隶属度、犹豫度和扩展记分函数组成,具有一些新的性质。将新的直觉模糊集(数)间相似度量方法应用于医疗方案优选,通过实例给出如何将原始数据转化为直觉模糊数据,之后用直觉模糊集间相似度量来解决该问题。实例表明,所提方法实用、有效。

Most existing similarity measures between intuitionistic fuzzy sets（values） do not take the hesitation degree into account, and the calculation method is only suitable for some certain situations. Based on the definition of intuitionistic fuzzy sets,a new similarity measure method is proposed. The new similarity formula is composed of membership degree, non membership degree, hesitation degree and expansion score function, and has some new properties. In this paper, the new similarity measure method between intuitionistic fuzzy sets（values） is applied to the medical scheme optimization. Through the examples, shows how to translate the raw data into intuitionistic fuzzy data, and to solve the problem with the new similarity measure method. Examples show that the proposed method is practical and effective.