CAJ | 학술논문

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。
침대혼돈신호소파거조중난이학정최우역치적문제，제출일충Volterra소파변환최우역치적판정방법。이용소파변환장혼돈신호분해，대불동척도하적소파신호설정부동인자이조절역치대소，최후근거혼돈서렬Volterra자괄응예측적상대오차선취최우역치。이용해방법대불동유도적Lorenz혼돈시간서렬진행료거조연구，결과표명소제방법시유효적。
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.