复杂过程分析的模糊聚类分类器设计及其性能  

Design of Fuzzy Classifier for Complicated Process Analysis and Its Performance

作　　者：郑慧民[1] 倪涛[1] 孙铁 瞿寿德[1] 

机构地区：[1]北京科技大学自动化系

出　　处：《北京科技大学学报》1993年第5期521-525,共5页Journal of University of Science and Technology Beijing

基　　金：国家自然科学基金

摘　　要：本文采用模式识别方法推断烧结矿质量。在给出模糊系统聚类分析算法基础上,用软件实现了基于模糊聚类分类器和动态聚类分类器,并用现场实测的样本采用“留一法”分别对这两种分类器性能进行检验。结果表明:模糊聚类分析法对于先验知识较少、样本量不大时,性能较佳。Application of pattern recognition and AI is a promisive approach to this problem. In the paper, pattern recognition method is used to test the quality of sinter. On the basis of the algorithm of fuzzy clustering analysis, the classifiers based on fuzzy clustering and dynamic state clustering in microcomputer are set up. The performance of the classifiers is tested. The results show that, in the case of lacking priori knowledge, fuzzy clustering analysis is superior to dynamic state clustering analysis when the number of samples is small.

关 键 词：模糊聚类分析 分类器 模式识别 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]