基于Vague集相似度量的模糊分类  

Fuzzy classification based on similarity measures between Vague sets

作　　者：许昌林[1] 魏立力[1] 

机构地区：[1]宁夏大学数学计算机学院

出　　处：《计算机工程与应用》2010年第36期161-164,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60663003)~~

摘　　要：在区间值模糊集理论和Vague集理论的基础上,提出了一种新的基于Vague集相似度量的模糊分类方法,对Fisher建立的Iris数据库进行了分类实验处理,分类结果的正确率超过95%。该方法计算简单有效,具有一定的实际应用价值。According to the interval-valued fuzzy set theory and the Vague sets theory,a new algorithm based on similarity measures between Vague sets is proposed and applied to the Iris data presented by Fisher.The result shows that more than 95% of the data are classified rightly.The method is simple and efficients,o it is useful for some real problems.

关 键 词：VAGUE集 相似度量 区间值模糊集 模糊分类 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]