摘要
在多属性决策分析中,传统的TOPSIS法是基于欧氏距离来计算各方案到正负理想点的距离,但欧氏距离没有考虑各属性之间的相关性;从这一角度出发,将相关系数矩阵与欧式距离结合,从而弥补了欧氏距离的不足,最后进行了实例分析.
Traditional TOPSIS method in multi-attribute decision analysis is based on the Euclidean distance in order to calculate the distance between the positive and negative ideal point,but the Euclidean distance does not consider the correlation between each attribute;From this perspective,this paper combines the correlation coefficient matrix with the Euclidean distance,which makes up the deficiencies of the Euclidean distance and finally illustrates it with an example.
出处
《数学的实践与认识》
CSCD
北大核心
2014年第4期33-38,共6页
Mathematics in Practice and Theory
基金
国家自然科学基金"两维语义的证据推理理论与系统研究"(71071048)
关键词
TOPSIS法
欧氏距离
马氏距离
相关系数矩阵
多属性决策
TOPSIS method
euclidean distance
mahalanobis distance
correlation coefficient matrix
multiple Attribute decision Making
