摘要
本文分析了Kosko等人提出的关于模糊子集的度量方法,提出其局限性,从而给出了一种新的模糊子集度量方法与模糊似然函数.由于这种方法能够更好地刻划模糊集合间的子集度与似然性,从而在模式识别、聚类分析、图像及信息处理中有着重要实际应用意义.同时文章也给出了两种新的模糊熵表示方法.
Based on the study of information measures on fuzzy sets, the fuzzy measures proposed by Kosko and Wang are modified in this paper. Moreover, fuzzy entropy is approached and two new entropy explanations are given. These results are of practical useness in pattern recognition, fuzzy clustering, information and image processing.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2001年第1期23-26,共4页
Pattern Recognition and Artificial Intelligence
关键词
模糊度量
模式识别
模糊似然函数
模糊熵
Fuzzy Measure, Pattern Recognition, Fuzy Likelihood, Fuzzy Entropy
