Ramdani Sofiane, Bouchara Frédéric, Lagarde Julien
EA 2991 Efficience et Déficience Motrices, Université de Montpellier I, Montpellier 34090, France.
Chaos. 2009 Mar;19(1):013123. doi: 10.1063/1.3081406.
We study the effect of static additive noise on the sample entropy (SampEn) algorithm [J. S. Richman and J. R. Moorman, Am. J. Physiol. Heart Circ. Physiol. 278, 2039 (2000); R. B. Govindan et al., Physica A 376, 158 (2007)] for analyzing time series. Using surrogate data tests, we empirically investigate the ability of the SampEn index to detect nonlinearity in simulated time series corrupted by increased amounts of noise. Discrete and continuous chaotic and nonchaotic systems are included in the numerical experiments. Both Gaussian and uniformly distributed noises are considered. The results indicate that the SampEn statistic is a robust index for detecting nonlinearity in time series corrupted by observational noise.
我们研究了静态加性噪声对用于分析时间序列的样本熵（SampEn）算法[J. S. 里奇曼和J. R. 莫尔曼，《美国生理学杂志：心脏和循环生理学》278, 2039 (2000); R. B. 戈文丹等人，《物理A》376, 158 (2007)]的影响。通过替代数据测试，我们实证研究了SampEn指标在检测被增加量噪声破坏的模拟时间序列中的非线性的能力。数值实验中包括离散和连续的混沌与非混沌系统。同时考虑了高斯噪声和均匀分布噪声。结果表明，SampEn统计量是检测被观测噪声破坏的时间序列中的非线性的一个稳健指标。
