Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Detecting and characterizing phase synchronization in nonstationary dynamical systems.

Ying-Cheng Lai1, Mark G Frei, Ivan Osorio

  • 1Department of Electrical Engineering, Arizona State University, Tempe, Arizona 85287, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 12, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Optimizing disorder with machine learning to harness phase synchronization.

Chaos (Woodbury, N.Y.)·2026
Same author

Controlling severe atopic dermatitis dynamics.

Chaos (Woodbury, N.Y.)·2026
Same author

Unsupervised Learning for Anticipating Critical Transitions.

Physical review letters·2026
Same author

Neuromorphic reservoir computing.

Chaos (Woodbury, N.Y.)·2025
Same author

Learning to learn ecosystems from limited data.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Generalized paradox of enrichment: noise-driven rare rarity in degraded ecological systems.

Journal of the Royal Society, Interface·2025

This study introduces a new framework for detecting phase synchronization in noisy, nonstationary time series. It offers methods to identify synchronization onset and quantify its degree, even correcting for noise effects.

Area of Science:

  • Nonlinear dynamics
  • Time series analysis
  • Complex systems

Background:

  • Phase synchronization is crucial in many complex systems, but its detection in noisy, nonstationary data is challenging.
  • Existing methods struggle to accurately characterize synchronization dynamics under varying conditions.

Purpose of the Study:

  • To develop a robust framework for detecting and characterizing phase synchronization in noisy, nonstationary time series.
  • To differentiate between noise-induced and coupling-induced desynchronization events.
  • To provide practical methods for correcting noise effects on phase synchronization measures.

Main Methods:

  • Utilizing average phase-synchronization time for sensitive detection of synchronization onset.
  • Employing phase diffusion evolution within a moving time window to characterize temporal synchronization.

Related Experiment Videos

  • Analyzing time scales of desynchronization events to distinguish noise from coupling effects.
  • Constructing a prototype model of coupled chaotic oscillators with time-varying coupling for validation.

    • Main Results:

    • The average phase-synchronization time proves highly sensitive to parameter changes at synchronization onset.
    • Phase diffusion monitoring offers a practically useful measure of temporal phase synchronization, improvable with window size.
    • Noise-induced desynchronization occurs on significantly shorter time scales than coupling-induced desynchronization.
    • A control study with a nonstationary dynamical system validates the proposed framework's effectiveness.

    Conclusions:

    • The proposed framework provides a reliable method for analyzing phase synchronization in complex, real-world time series.
    • The distinct time scales of desynchronization mechanisms allow for effective noise correction.
    • This work advances the understanding and analysis of synchronization phenomena in dynamic systems.