• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2010, Vol. 32 ›› Issue (7): 93-94.doi: 10.3969/j.issn.1007130X.2010.

• 论文 • 上一篇    下一篇

一种利用信息熵确定属性权重的模糊单因素评价方法

魏书堤,姜小奇   

  1. (衡阳师范学院计算机科学系,湖南 衡阳 421008)
  • 收稿日期:2009-11-18 修回日期:2010-02-25 出版日期:2010-06-25 发布日期:2010-06-25
  • 通讯作者: 魏书堤 E-mail:weishudi@126.com
  • 作者简介:魏书堤(1969),男,湖南衡阳人,硕士,副教授,研究方向为决策管理系统;姜小奇,博士生,研究方向为决策管理系统。

A SingleFactor Fuzzy Evaluation Method of Using Information Entropy to Determine the Property Weights

WEI Shudi,JIANG Xiaoqi   

  1. (Department of Computer Science,Hengyang Normal University,Hengyang 421008,China)
  • Received:2009-11-18 Revised:2010-02-25 Online:2010-06-25 Published:2010-06-25
  • Contact: WEI Shudi E-mail:weishudi@126.com

摘要:

在多属性决策问题中,由于问题的复杂性,属性的权重一般是未知量或者只有部分信息权重,研究如何确定多属性决策问题中属性的权重,以便对已有的方案进行排序或评价,已经成为多属性决策研究的一个重点问题。截止目前,多属性权重确定方法主要包括主观权重确定法和客观权重确定法,主观权重确定法具有受决策主体主观偏好影响的缺点,而客观权重确定方法往往忽略决策主体的参与程度。因此,如何研究将主客观权重复制相结合,提高决策的准确性,具有实际的研究意义。本文总结了多属性决策问题中权重的确定方法,提出了一种主观赋值与客观确定相结合的改进熵值权重确定方法,并通过实例证明了该算法的有效性。

关键词: 熵, 最小平方法, 隶属度

Abstract:

In multiattribute decisionmaking,because of the complexity of the problem,the weights of attributes are  generally unknown or some information of the weights known.How to determine the weights of multiattribute decision making,sort or evaluate the existing scheme has become a priority issue of multiattribute decisionmaking.Up to now,the method to determine the weights of the multiattribute decisionmaking includes the subjective and objective methods.The subjective method has the shortcomings of subjective preferences and the objective method tends to ingore the level of the participation of the decision makers.Therefore,how to study the combination of subjective and objective weightes and improve the accuracy of decisionmaking has practical research significance.This paper summarizes the methods of weight determination in the multiattribute decisionmaking,and proposes method of the combination of subjective assignment and objective determination to  improve the entropy weight.With an example,the validity of algorithm is proved.

Key words: entropy;least squares;membership