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DENOISING METHOD BASED ON SINGULAR SPECTRUM ANALYSIS AND ITS APPLICATIONS IN CALCULATION OF MAXIMAL LIAPUNOV EXPONENT

机译:基于奇异谱分析的降噪方法及其在最大Liapunov指数计算中的应用

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摘要

An algorithm based on the data-adaptive filtering characteristics of singular spectrum analysis (SSA) is proposed to denoise chaotic data. Firstly, the empirical orthogonal functions ( EOFs ) and principal components ( PCs ) of the signal were calculated, reconstruct the signal using the EOFs and PCs, and choose the optimal reconstructing order based on sigular spectrum to obtain the denoised signal. The noise of the signal can influence the calculating precision of maximal Liapunov exponents. The proposed denoising algorithm was applied to the maximal Liapunov exponents calculations of two chaotic system, Henon map and Logistic map. Some numerical results show that this denoising algorithm could improve the calculating precision of maximal Liapunov exponent.
机译:提出了一种基于奇异谱分析(SSA)数据自适应滤波特性的混沌数据去噪算法。首先,计算信号的经验正交函数(EOF)和主成分(PC),使用EOF和PC重构信号,并基于信号频谱选择最佳重构顺序以获得降噪后的信号。信号的噪声会影响最大Liapunov指数的计算精度。将所提出的去噪算法应用于Henon图和Logistic图这两个混沌系统的最大Liapunov指数计算。数值结果表明,该去噪算法可以提高最大Liapunov指数的计算精度。

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