%0 Journal Article
%T Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine
%A TANG Xian-lun ZHUANG Ling QIU Guo-qing CAI Jun
%A <br>TANG Xianlun
%A ZHUANG Ling
%A QIU Guoqing
%A CAI Jun
%J 重庆邮电大学学报(自然科学版)
%D 2009
%I 
%X 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.
%K support vector machine
%K particle swarm optimization
%K chaos
%K fault diagnosis<br>最小二乘支持向量机
%K 粒子群优化算法
%K 故障诊断
%K 旋转机械
%K 混沌
%K 多故障分类
%K 神经网络训练
%K 最佳参数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=5C2694A2E5629ECD6B59D7B28C6937AD&aid=205A1FDBD0F5E6416841E5412315254E&yid=DE12191FBD62783C&vid=659D3B06EBF534A7&iid=0B39A22176CE99FB&sid=B344543C2864D684&eid=76B5E24D6EC46B4B&journal_id=1673-825X&journal_name=重庆邮电大学学报(自然科学版)&referenced_num=0&reference_num=16