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《Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)》 2009-02
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Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

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.China)  
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 classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine,and the precision and reliability of the fault classification results can meet the requirement of practical application.It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.
【Fund】: supported by the National Nature Science Foundation of China under Grant 60506055
【CateGory Index】: TH165.3
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