基于样本熵理论的自适应小波消噪分析方法
Adaptive wavelet denoising method based on sample entropy
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摘要: 小波消噪方法的核心问题是阈值的选择及确定.依据样本熵的特性,将样本熵与小波分析方法耦合起来,提出了一种自适应确定阈值的小波消噪分析方法.该方法计算了不同阈值对应噪声序列的样本熵值,得到阈值与样本熵值之间的关系曲线,当样本熵值达到最大时,此时阈值为所求阈值.通过算例加以验证分析,结果表明,自适应小波消噪分析方法能较好地实现水文序列的信噪分离,其消噪结果符合序列本身的特性及评价指标的要求,这为合理确定阈值提供了一种新的途径和方法.Abstract: It is a key problem of properly selecting and determining the threshold value for wavelet denoising.In this paper,a new method is proposed to adaptively determine the threshold for wavelet analysis.The method uses the character of sample entropy and combines it with wavelet denoising method.Using the method,the sample entropy values are computed for noise time series with different threshold values.The relationship between the sample entropy values and the corresponding threshold values can thus be obtained.As the result,the threshold value is determined at the point with maximum entropy.The method is tested using the synthetic data and annual runoff series from Yingluoxia on the Heihe River basin and Huayuankou on the Yellow River basin.Results show that the method is able to separate the signal and noise from hydrological time series with a good denoising performance.The resulting signal is able to retain the characteristics of hydrological time series and evaluation indexes.The method also offers a new approach to determine the threshold value for wavelet denoising.