Algorithm for Detecting Deterministic Chaos in Pseudoperiodic Time Series
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).
How to Cite
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.
By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.