Abstract
The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on chaotic data noise reduction. In the paper, we first decompose the phase space of chaotic time series to range space and null noise space. Secondly we restructure original chaotic time series in range space. Lastly on the basis of the above, we establish order of the nonlinear model and make use of the nonlinear model to predict some research. The result indicates that the nonlinear model has very strong ability of approximation function, and Chaos predict method has certain tutorial significance to the practical problems.
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Project supported by the National Natural Science Foundation of China (Nos.70271071, 19990510, D0200201)
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Ma, JH., Wang, ZQ. & Chen, Ys. Prediction techniques of chaotic time series and its applications at low noise level. Appl Math Mech 27, 7–14 (2006). https://doi.org/10.1007/s10483-006-0102-1
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DOI: https://doi.org/10.1007/s10483-006-0102-1