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Abstract

Affected by some factors such as external forcing and the measurement errors of instrument itself, observational data often contain noises, disturbances and some other false information. To solve this problem, the effects of different noises on moving cut data-approximate entropy (MC-ApEn) are investigated in this paper. The results indicate that MC-ApEn is little affected by random spikes and Gaussian white noise, which means that the MC-ApEn method has strong anti-noise ability. The results provide an essential experimental basis for the wide applications of the present method to observational data.

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Funds:Project supported by the National Basic Research Program of China (Grant No. 2012CB955203), the National Natural Science Foundation of China (Grant Nos. 40930952, 41175067 and 40905034), the Special Scientific Research Fund of Public Welfare Profession of China (Grant Nos. GYHY201106016 and GYHY201106015), and the State Key Program of Science and Technology of China (Grant Nos. 2007BAC29B01, 2009BAC51B04).

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Vol. 61, Issue 12 - 2012-06-20

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