Identifying complex periodic windows in continuous-time dynamical systems using recurrence-based methods

TL;DR

This paper introduces recurrence plot-based nonlinear measures, including recurrence networks, as effective tools for identifying complex periodic windows in continuous-time dynamical systems, especially with short data series.

Contribution

It demonstrates that recurrence-based measures, such as average path length and clustering coefficient, can reliably distinguish periodic from chaotic behavior in continuous systems.

Findings

Recurrence network measures effectively classify system dynamics.

Average path length and clustering coefficient are powerful discriminators.

Method works well with short time series.

Abstract

The identification of complex periodic windows in the two-dimensional parameter space of certain dynamical 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 line-based 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. Using the well-studied R\"ossler system as a benchmark example, we find that the average path length and the clustering coefficient of the resulting recurrence networks (RNs) are particularly powerful discriminatory statistics for the identification of complex periodic windows.