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 ]
Su, Rijian [1 ,2 ]
Song, Shengli [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
[2] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou, Peoples R China
来源：
PROCEEDINGS OF THE 2011 INTERNATIONAL CONFERENCE ON INFORMATICS, CYBERNETICS, AND COMPUTER ENGINEERING (ICCE2011), VOL 2: INFORMATION SYSTEMS AND COMPUTER ENGINEERING | 2011年 / 111卷
关键词：
Sets Patition; Entropy; Improvement Swarm Optimization; Membershipfunction;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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：323 / +
页数：2
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