Please use this identifier to cite or link to this item: https://doi.org/10.1088/0305-4470/33/5/313
DC FieldValue
dc.titleDetecting chaos from time series
dc.contributor.authorXiaofeng, G.
dc.contributor.authorLai, C.H.
dc.date.accessioned2014-10-16T09:20:29Z
dc.date.available2014-10-16T09:20:29Z
dc.date.issued2000-02-11
dc.identifier.citationXiaofeng, G., Lai, C.H. (2000-02-11). Detecting chaos from time series. Journal of Physics A: Mathematical and General 33 (5) : 1007-1016. ScholarBank@NUS Repository. https://doi.org/10.1088/0305-4470/33/5/313
dc.identifier.issn03054470
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/96181
dc.description.abstractIn this paper, an entirely data-based method to detect chaos from the time series is developed by introducing ∈p-neighbour points (the p-steps ∈-neighbour points). We demonstrate that for deterministic chaotic systems, there exists a linear relationship between the logarithm of the average number of ∈p-neighbour points, ln np,∈, and the time step, p. The coefficient can be related to the KS entropy of the system. The effects of the embedding dimension and noise are also discussed.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentPHYSICS
dc.description.doi10.1088/0305-4470/33/5/313
dc.description.sourcetitleJournal of Physics A: Mathematical and General
dc.description.volume33
dc.description.issue5
dc.description.page1007-1016
dc.identifier.isiutNOT_IN_WOS
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