模糊模式识别的基础——相似度量  

BASIS OF FUZZY PATTERN RECOGNITION-SIMILARITY MEASURES

作　　者：刘华文[1] 

机构地区：[1]山东大学数学与系统科学学院,济南250100

出　　处：《模式识别与人工智能》2004年第2期141-145,共5页Pattern Recognition and Artificial Intelligence

基　　金：山东省自然科学基金(No.Y2001G07)

摘　　要：讨论模糊模式识别中模糊集之间的相似度量.针对模糊集的推广形式——Vague集,给出Vague集之间一系列新的相似度量方法,并对其性质进行讨论.通过与现有方法的比较,阐明该方法有较强的分辨能力.对连续论域的情况一并作了讨论,给出相应度量的积分表示.最后,用例子说明Vague集之间的相似度量在模式识别中的应用.The problem of similarity measure between fuzzy sets in pattern recognition is discussed. A set of newmethods for measuring the degree of similarity between vague sets as a generation of Zadeh's fuzzy sets is proposedand their properties are discussed. These new methods are illustrated by comparison with the present measure methodsthat they have stronger discrimination. Furthermore, a set of corresponding methods for measuring the degree ofsimilarity between vague sets in a continuous universe is given. Finally, the similarity measures between vaguesets are applied to pattern recognition.

关 键 词：模糊集 VAGUE集 直觉模糊集 相似度量 模式识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]