Detecting Deterministic Structure from a High Noisy Pseudoperiodic Time Sseries

Abstract

The distinguishing between high-noisy chaotic pseudoperiodic time series and high-noisy periodic or quasiperiodic time series primarily enhanced by the nonlinear noise reduction methods is investigated. The different algorithm is described to 80 detect deterministic structure from a pseudoperiodic time series enhanced by the singular value decomposition method. The algorithm is more robust for distortion of nonlinear noise reduction than widely used Lyapunov exponent. Similar to the Lyapunov exponent, the algorithm is based on the divergence of the nearest neighbors, but the averaged dynamic of amount of initial vector pairs, that satisfy the condition of the nearest neighbors, is calculated instead of the dynamic of distance between the vector pairs. By combining with nonlinear noise reduction methods the proposed algorithm can distinguish reliable between regular and chaotic pseudoperiodic time series, contaminated by additive white Gaussian noise with SNR up to 0 dB. Also, the difference of the recurrence properties between enhanced noisy pseudoperiodic chaotic and enhanced regular signals is analyzed. It is concluded, that the histograms of white vertical lines of the recurrency plots (RP) allow to distinguish chaotic signal enhanced by nonlinear noise reduction method from enhanced regular sinusoidal signal at a signal-additive white Gaussian noise ratio up to 0 dB. Ill 4, bibl. 18 (in English; summaries in English, Russian and Lithuanian).