Algorithm for Detecting Deterministic Chaos in Pseudoperiodic Time Series

K. Pukėnas, K. Muckus

Abstract


A new straightforward algorithm is proposed to detect deterministic structure from a pseudoperiodic time series without embed-ding. The correlation coefficient as a measure of the distance between certain cycles is used and averaged diverging slope between clos-est cycles, as an indicator of chaos, is calculated according well known Rosenstein method for calculating largest Lyapunov exponents. We demonstrate that this method can reliably identify chaos in the presence of noise of different sources at a signal-noise ratio up to 10 dB for both artificial data and experimental time series. Ill 3, bibl. 11 (in English; summaries in English, Russian and Lithuanian).


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Print ISSN: 1392-1215
Online ISSN: 2029-5731