Vague集相似度量模型  

A new Similarity Measure Model Between Vague Sets

作　　者：彭祖明[1] 陈义华[2] 

机构地区：[1]长江师范学院数学与计算机学院,重庆408100 [2]重庆大学数理学院,重庆400044

出　　处：《数学的实践与认识》2013年第11期215-220,共6页Mathematics in Practice and Theory

摘　　要：Vague集的相似度量在模糊推理、模式识别、聚类分析、决策分析等领域的广泛运用,要求所建立的vague集相似度量模型具有较高的区分度及度量结果合乎人的直觉.基于此要求,首先对已有Vague值的相似度量模型在区分度上的不足进行了分析.然后,在分析地基础上,提出了vague值的相似度量建模须考虑的因素.最后建立了Vague集的相似度量模型.数值实验表明,新模型具有较好的区分度,能克服已有模型在区分度上的不足.The similarity measurement of vague sets is widely applied in the field of fuzzy reasoning, pattern recognition, cluster analysis, decision analysis, etc. The application need that the similarity measure model between vague sets has a good degree of discrimination, and the measurement results can accord with the intuition of human being. Based on this require- ment, first, the shortcoming of existing similarity measure models between vague values was analyzed. Secondly, the necessary factors which are considered in the modeling were pointed out, then a new similarity measure model of vague sets was established. The experimental results show that the proposed model had good discrimination, can overcome the shortcoming of the existing model.

关 键 词：VAGUE集 相似度量 模型 直觉 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]