首页|期刊导航|重庆邮电大学学报（自然科学版）|Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine
重庆邮电大学学报（自然科学版）2009，Vol.21Issue(2)：127-133,7.
Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine
Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine
摘要
Abstract
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.关键词
support vector machine/particle swarm optimization/chaos/fault diagnosisKey words
support vector machine/particle swarm optimization/chaos/fault diagnosis分类
信息技术与安全科学引用本文复制引用
TANG Xian-lun,ZHUANG Ling,QIU Guo-qing,CAI Jun..Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine[J].重庆邮电大学学报（自然科学版）,2009,21(2):127-133,7.基金项目
This work was supported by the National Nature Science Foundation of China under Grant 60506055. （）
