Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machineOA北大核心CSTPCD
Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine
The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm …查看全部>>
The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault cla…查看全部>>
TANG Xian-lun;ZHUANG Ling;QIU Guo-qing;CAI Jun
Key Laboratory of Network control & Intelligent Instrument /Chongqing University of Posts and Telecommunications, Ministry of Education Chongqing University of Posts and Telecommunications, Chongqing 400065,P. R. ChinaKey Laboratory of Network control & Intelligent Instrument /Chongqing University of Posts and Telecommunications, Ministry of Education Chongqing University of Posts and Telecommunications, Chongqing 400065,P. R. ChinaKey Laboratory of Network control & Intelligent Instrument /Chongqing University of Posts and Telecommunications, Ministry of Education Chongqing University of Posts and Telecommunications, Chongqing 400065,P. R. ChinaKey Laboratory of Network control & Intelligent Instrument /Chongqing University of Posts and Telecommunications, Ministry of Education Chongqing University of Posts and Telecommunications, Chongqing 400065,P. R. China
信息技术与安全科学
support vector machineparticle swarm optimizationchaosfault diagnosis
support vector machineparticle swarm optimizationchaosfault diagnosis
《重庆邮电大学学报（自然科学版）》 2009 (2)
127-133,7
This work was supported by the National Nature Science Foundation of China under Grant 60506055.
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