Volterra小波变换最优阈值的混沌去噪方法

Chaotic Signal Denoising Based on Optimal Threshold of Volterra and Wavelet Transforms

针对混沌信号小波去噪中难以确定最优阈值的问题,提出一种Volterra小波变换最优阈值的判定方法。利用小波变换将混沌信号分解,对不同尺度下的小波信号设定浮动因子以调节阈值大小,最后根据混沌序列Volterra自适应预测的相对误差选取最优阈值。利用该方法对不同维度的Lorenz混沌时间序列进行了去噪研究,结果表明所提方法是有效的。

How to determine the optimal threshold of chaotic signal based on wavelet transform is an important topic in chaotic identification.In this paper,a method for choosing the optimal threshold based on Volterra and wavelet transform is proposed.The chaotic signal is decomposed by wavelet transform.Then,a floating parameter is set to regulate the threshold of the wavelet signal in different scales according to the prediction error of the chaotic time series from the Volterra adaptive prediction.Denoising for chaotic time series generated by Lorenz system is simulated and the result is compared with those of the other methods.It is shown that the proposed method is effective.