Publications
Chaos, 20(4), 043130 (2010) DOI:10.1063/1.3523304
Identifying complex periodic windows in continuoustime dynamical systems using recurrencebased methods
Y. Zou, R. V. Donner, J. F. Donges, N. Marwan, J. Kurths
The identification of some specific periodic islands (socalled shrimps) in the twodimensional parameter space of certain complex systems has recently attracted considerable interest. While for discrete systems, a discrimination between periodic and chaotic windows can be easily made based on the maximum Lyapunov exponent of the system, this remains a challenging task for continuous systems, especially if only short time series are available (e.g., in case of experimental data). In this work, we demonstrate that nonlinear measures based on recurrence plots obtained from such trajectories provide a practicable alternative for numerically detecting shrimps. Traditional diagonal linebased measures of recurrence quantification analysis (RQA) as well as measures from complex network theory are shown to allow an excellent classification of periodic and chaotic behavior in parameter space. Average path length and clustering coefficient of the resulting recurrence networks (RNs) are found to be particularly powerful discriminatory statistics for the identification of shrimps in the Rössler system.
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