噪声对滑动移除近似熵的影响

摘要

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.%受外强迫和仪器本身的测量误差等因素的影响，观测数据中经常包含噪声和扰动等一些虚假的信息．针对这一问题，本文研究了各种不同噪声对滑动移除近似熵的影响．研究结果表明，滑动移除近似熵的检测结果受尖峰噪声和高斯白噪声的影响较小，意味着其具有很强的抗噪能力，这为该方法在实际观测资料中的广泛应用提供了坚实的实验基础．