Prediction of Chaotic Time Series with Noise
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概要
- 論文の詳細を見る
In this paper, we propose algorithm of deterministic nonlinear prediction, or a modified version of the method of analogues which was originally proposed by E. N. Lorenz (J. Atom. Sci., 26, 636-646, 1969), and apply it to the artificial time series data produced from nonlinear dynamical systems and further corrupted by superimposed observational noise. The prediction performance of the present method are investigated by calculating correlation coefficients, root mean square errors and signature errors and compared with the prediction algorithm of local linear approximation method. As a result, it is shown that the prediction performance of the proposed method are better than those of the local linear approximation especially in case that the amount of noise is large.
- 社団法人電子情報通信学会の論文
- 1995-10-25
著者
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Aihara K
Univ. Tokyo Tokyo Jpn
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Aihara K
Department Of Complexity Science And Engineering Graduate School Frontier Sciences The University Of
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AIHARA Kazuyuki
Faculty of Engineering, The University of Tokyo and CREST, Japan Science and Technology Corporation
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Ikeguchi Tohru
Faculty of Industrial Science and Technology, Science University of Tokyo
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Ikeguchi T
Department Of Information And Numerical Sciences Graduate School Of Science And Engineering Saitama
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Ikeguchi Tohru
Faculty Of Engineering Saitama University:graduate School Of Science And Engineering Saitama Univers
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Aihara Kazuyuki
Faculty Of Engineering The University Of Tokyo
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