基于在线最小二乘支持向量机回归的混沌时间序列预测

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、Hénon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. 关键词： 混沌时间序列 预测 在线学习 支持向量机

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

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, Rssler system, Hénon map an d chaotic electroencephalogram (EEG) signal, are used to evaluate the performanc e. The results verify the ability of the method in chaotic time series predictio n.