The Research and Application of LS_SVM Based on Particle Swarm Optimization

摘要：

To select parameters is important in the research area of support vector machine. Based on particle swarm optimization, this paper proposes automatic parameters selection for least squares support vector machine (LS_SVM). The effect of this proposed method is demonstrated by function regression problem. Besides, an equipment fault classification further illustrates that LS_SVM based on particle swarm optimization has better classification ability than LS_SVM based genetic algorithm under the same condition.

关键词： least squares support vector machine particle swarm optimization fault classification

作者: Yongqi Chen Zhanxin Zhou Qijun Chen

作者单位: Department of Information and Control Engineering Tongji University Shanghai, China;School of Scienc Department of Information and Control Engineering Tongji University Shanghai, China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）