中国物理B ›› 2004, Vol. 13 ›› Issue (4): 454-458.doi: 10.1088/1009-1963/13/4/007
汪晓东1, 叶美盈2
Ye Mei-Ying (叶美盈)a, Wang Xiao-Dong (汪晓东)b
摘要: We propose a new technique of using the least squares support vector machines (LS-SVMs) for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction. Unlike neural networks' training that requires nonlinear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Thus it has fast convergence. The simulation results show that LS-SVM has much better potential in the field of chaotic time series prediction.
中图分类号: (Time series analysis)