一个新的基于Vague集加权相似度量的双向近似推理方法  

Bidirectional Approximate Reasoning Based on Weighted Similarity Measures of Vague Set

作　　者：王天江[1] 卢正鼎[1] 李凡[1] 

机构地区：[1]华中科技大学计算机学院,湖北武汉430074

出　　处：《小型微型计算机系统》2004年第2期211-215,共5页Journal of Chinese Computer Systems

基　　金：国家高性能计算基金 (0 0 3 0 3 )资助;华中科技大学科学研究基金 (M990 15 )项目资助

摘　　要：提出了一种新的 Vague集的加权相似度量方法以解决文献 [1]中关于 Vague集相似度量的某些缺陷 ,并且提出了 Vague集间相似方向的概念 ,用它来描述两个相似 Vague集中哪个所包含的信息更精确 ,并给出了一个判定方法 .在此基础上给出了一种基于 Vague集加权相似度量的双向近似推理方法 ,该方法更好地利用了 Vague集信息的精确性 ,从而提高了推理的精确性和适用性 .这为智能系统中的近似推理提供了一个十分有用的工具 .In this paper, we introduce a new weighted similarity measure for vague set in order to solve some faults in [1] , and propose the concept of similarity direction between two vague sets by which to describe which one could give more accurate information. At the same time, we propose a method for determining the similarity direction. Based on above, we present a bidirectional approximate reasoning method based on weighted similarity measures of vague set, which fully uses the message accuracy of vague set. Then this method improves the accuracy and applicability of approximate reasoning, and also provides a useful tool for approximate reasoning in intelligence system.

关 键 词：FUZZY集 VAGUE集 加权相似度量 相似方向 双向近似推理 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]