Improving the prediction of chaotic time series

被引：0
|
作者：
Li, KP [1 ]
Gao, ZY
Chen, TL
机构：
[1] No Jiaotong Univ, Inst Syst Sci, Beijing 100044, Peoples R China
[2] Nankai Univ, Dept Phys, Tianjin 300071, Peoples R China
来源：
CHINESE PHYSICS | 2003年 / 12卷 / 11期
关键词：
chaotic time series; local Lyapunov exponent; neighbouring point; neural network;
D O I：
暂无
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
One of the features of deterministic chaos is sensitive to initial conditions. This feature limits the prediction horizons of many chaotic systems. In this paper, we propose a new prediction technique for chaotic time series. In our method, some neighbouring points of the predicted point, for which the corresponding local Lyapunov exponent is particularly large, would be discarded during estimating the local dynamics, and thus the error accumulated by the prediction algorithm is reduced. The model is tested for the convection amplitude of Lorenz systems. The simulation results indicate that the prediction technique can improve the prediction of chaotic time series.
引用
收藏
页码：1213 / 1217
页数：5
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