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Chin. Phys., 2003, Vol. 12(11): 1213-1217    DOI: 10.1088/1009-1963/12/11/005
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Improving the prediction of chaotic time series

Chen Tian-Luna, Li Ke-Pingb, Gao Zi-Youb
a Department of Physics, Nankai University, Tianjin 300071, China; b Institute of Systems Science, Northern Jiaotong University, Beijing 100044, China
Abstract  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.
Keywords:  neural network      chaotic time series      local Lyapunov exponent      neighbouring point  
Received:  04 March 2003      Revised:  16 June 2003      Published:  16 March 2005
PACS:  05.45.Tp (Time series analysis)  
  05.45.Pq (Numerical simulations of chaotic systems)  
Fund: Project supported by the Funds for Outstanding researchers from the National Natural Science Foundation of China (Grant No 70225005), and Research Award Program (2001) for Outstanding Young Teachers in Higher Education Institutions of Ministry of Educatio

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Chen Tian-Lun, Li Ke-Ping, Gao Zi-You Improving the prediction of chaotic time series 2003 Chin. Phys. 12 1213

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