基于直觉模糊粗糙集相似度的多属性决策方法

Multi-attribute decision making method based on improved similarity measure of intuitionistic fuzzy rough sets

将直觉模糊粗糙集应用于多属性决策问题,提出了基于改进的直觉模糊粗糙集相似度的多属性决策方法。针对现有的直觉模糊粗糙集相似度忽略犹豫度而造成度量不精确的问题,提出了一种改进的直觉模糊粗糙集相似性度量方法,并揭示其若干重要性质。在此基础上,将属性值用直觉模糊粗糙集表示,并通过各个方案与直觉模糊粗糙集正、负理想方案的相似度比较,实现决策方案排序。数值实例表明了该方法的可行性和有效性,其在态势评估、目标识别等信息融合领域有良好的应用前景。

Intuitionistic Fuzzy Rough Sets（IFRS） are applied to the problems of Multi-Attribute Decision Making （MADM）, and the method of MADM base on the improved similarity measure of IFRS is presented. Firstly, the im-proved similarity measure of IFRS is proposed which conquers the question of accurate degree of similarity measure by adding the hesitancy degree, and several important characters of it are revealed. Furthermore, the new method compares the alternatives with positive and negative ideal solution to realize alternative ranking, whose attribute values are consid-ered as IFRS. At last, the practical example shows the feasibility and effectiveness of the proposed method, which has the preferable application foreground in information fusion field, such as situation assessment and target recognition.