Modified correlation entropy estimation for a noisy chaotic time series

International Centre for Water Hazard and Risk Management, Public Works Research Institute, 305-8516 Tsukuba, Japan.
Chaos (Woodbury, N.Y.) (Impact Factor: 1.95). 06/2010; 20(2):023104. DOI: 10.1063/1.3382013
Source: PubMed


A method of estimating the Kolmogorov-Sinai (KS) entropy, herein referred to as the modified correlation entropy, is presented. The method can be applied to both noise-free and noisy chaotic time series. It has been applied to some clean and noisy data sets and the numerical results show that the modified correlation entropy is closer to the KS entropy of the nonlinear system calculated by the Lyapunov spectrum than the general correlation entropy. Moreover, the modified correlation entropy is more robust to noise than the correlation entropy.

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Available from: A. W. Jayawardena, Feb 03, 2014
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    • "Lyapunov exponents are very difficult, if not impossible , to be estimated when analyzing data coming from complex systems. Therefore, alternative ways to calculate the KS entropy, instead of applying Pesin's identity, should be taken into consideration [17], [18]. In this work we investigate the properties of a novel measure based on information theory, called permutation entropy (PE) [19], as an alternative to estimate the unpredictability of chaotic time traces. "
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