An Algorithm of Maximum Entropy Fuzzy Clustering Based on Improved Particle Swarm Optimization

被引：0
|
作者：
Su, Rijian [1 ,2 ]
Kong, Li [1 ]
Cheng, Jingjing [1 ]
Song, Shengli [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan, Peoples R China
[2] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou, Peoples R China
来源：
2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL II | 2010年
基金：
国家高技术研究发展计划(863计划);
关键词：
Information Sets Patition; Entropy; Improvement Swarm Optimization; Membershipfunction;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
for more clear understanding of the measurand, enough information must be obtained in a limited time and space. an improved particle swarm optimization (IPSO) is proposed, and the attempt to use an IPSO algorithm for fuzzy clustering to achieve the partition information sets. In the process of partition, the concept of Shannon entropy is introduced. The information set is divided into subsets according to measurement information entropy and the constituted objective function, and its essence is the use of different sensors to obtain a greater amount of information. The method is applied to logging data set partition.
引用
收藏
页码：157 / 160
页数：4
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