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A Novel Adaptive Predictor for Chaotic Time Series |
BU Yun1, WEN Guang-Jun1, ZHOU Xiao-Jia2, ZHANG Qiang3 |
1School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 6100542College of Automation, University of Electronic Science and Technology of China, Chengdu 6100543School of Sciences, Southwest Petroleum University, Chengdu 610500 |
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Cite this article: |
BU Yun, WEN Guang-Jun, ZHOU Xiao-Jia et al 2009 Chin. Phys. Lett. 26 100502 |
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Abstract Many chaotic time series show non-Gaussian distribution, and non-Gaussianity can be characterized not only by higher-order cumulants but also by negative entropy. Since negative entropy can be accurately approximated by some special non-polynomial functions, which also can form an orthogonal system, these functions are used to construct an adaptive predictor to replace higher-order cumulants. Simulation shows the algorithm performs well for different chaotic systems.
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Keywords:
05.45.+b
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Received: 19 February 2009
Published: 27 September 2009
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