基于在线最小二乘支持向量机回归的混沌时间序列预测  

Chaotic time series forecasting using online least squares support vector machine regression

作　　者：叶美盈[1] 汪晓东[2] 张浩然[2] 

机构地区：[1]浙江师范大学数理学院,金华321004 [2]浙江师范大学信息科学与工程学院,金华321004

出　　处：《物理学报》2005年第6期2568-2573,共6页Acta Physica Sinica

基　　金：浙江省自然科学基金(批准号:602145)资助的课题.~~

摘　　要：提出了一种基于在线最小二乘支持向量机(LS SVM)回归的混沌时间序列的预测方法.与离线支持向量机相比,在线最小二乘支持向量机预测方法即使当混沌系统的参数随时间变化时仍然有效.以Chen’s混沌系统、R ssler混沌系统、H啨non映射及脑电(EEG)信号四种混沌时间序列为例评估本文提出的预测方法,结果验证了其混沌时间序列预测的有效性.A chaotic time series forecasting method based on online least squares support vector machine (LS-SVM) regression is proposed. The difference between the online LS-SVM and offline support vector machine (SVM) is that the online LS-SVM is still effective for the chaotic system with a variation of the system parameter. Four chaotic time series, namely, Chen's system, Rossler system, Henon map and chaotic electroencephalogram (EEG) signal, are used to evaluate the performance. The results verify the ability of the method in chaotic time series prediction.

关 键 词：混沌时间序列 在线学习 支持向量机 回归预测 

分 类 号：O212[理学—概率论与数理统计]