Please use this identifier to cite or link to this item: https://doi.org/10.1088/0305-4470/33/5/313
Title: Detecting chaos from time series
Authors: Xiaofeng, G. 
Lai, C.H. 
Issue Date: 11-Feb-2000
Citation: Xiaofeng, 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
Abstract: In 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.
Source Title: Journal of Physics A: Mathematical and General
URI: http://scholarbank.nus.edu.sg/handle/10635/96181
ISSN: 03054470
DOI: 10.1088/0305-4470/33/5/313
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