Recurrence analysis of slow–fast systems

Abstract

Many complex systems exhibit periodic oscillations comprising slow–fast timescales. In such slow–fast systems, the slow and fast timescales compete to determine the dynamics. In this study, we perform a recurrence analysis on simulated signals from paradigmatic model systems as well as signals obtained from experiments, each of which exhibit slow–fast oscillations. We find that slow–fast systems exhibit characteristic patterns along the diagonal lines in the corresponding recurrence plot (RP). We discern that the hairpin trajectories in the phase space lead to the formation of line segments perpendicular to the diagonal line in the RP for a periodic signal. Next, we compute the recurrence networks (RNs) of these slow–fast systems and uncover that they contain additional features such as clustering and protrusions on top of the closed-ring structure. We show that slow–fast systems and single timescale systems can be distinguished by computing the distance between consecutive state points on the phase space trajectory and the degree of the nodes in the RNs. Such a recurrence analysis substantially strengthens our understanding of slow–fast systems, which do not have any accepted functional forms.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2020
Source ID
10.1063/1.5144630

Entities

People

  • Abin Krishnan
  • Induja Pavithran
  • Jürgen Kurths
  • Norbert Marwan
  • Praveen Kasthuri
  • R I Sujith
  • Rohan Gejji
  • Samadhan A Pawar
  • William E Anderson

Organizations

  • Air Force Office of Scientific Research
  • Humboldt-Universität zu Berlin
  • Indian Institute of Technology Madras
  • Potsdam Institute for Climate Impact Research
  • Purdue University

Tags

Readers

  • Control Systems Engineering.
  • Distributed Systems and Data Platform Development
  • Traumatic Brain Injury (TBI) and Cognitive Aging in the Guam and Border Populations Affected by Alzheimer's Disease and Tau-Associated Dementias.

Technology Areas

  • Space