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

Estimation of parameters in nonlinear systems using balanced synchronization.

Henry D I Abarbanel1, Daniel R Creveling, James M Jeanne

  • 1Department of Physics and Marine Physical Laboratory ,Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093-0402, USA. habarbanel@ucsd.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 21, 2008
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

Neural dynamics: Keeping track of time in the fly mushroom body.

Current biology : CB·2026
Same author

Sensory processing reformats odor coding around valence and dynamics.

bioRxiv : the preprint server for biology·2025
Same author

Divergent synaptic dynamics originate parallel pathways for computation and behavior in an olfactory circuit.

Current biology : CB·2025
Same author

Model of the HVC neural network as a song motor in zebra finch.

Frontiers in computational neuroscience·2024
Same author

Olfactory sensory neuron population expansions influence projection neuron adaptation and enhance odour tracking.

Nature communications·2024
Same author

Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers.

Chaos (Woodbury, N.Y.)·2023
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

This study introduces two novel methods for improving model parameter estimation using data synchronization. These techniques balance synchronization accuracy with the ability to reliably determine model parameters, even with noisy data.

Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Data Assimilation

Background:

  • Model parameter estimation often relies on synchronization between observed data and model simulations.
  • Strong coupling can lead to a minimized cost function but hinders accurate parameter determination.
  • Existing methods struggle to balance synchronization quality with parameter identifiability.

Purpose of the Study:

  • To develop and evaluate new methods for balanced synchronization in model parameter estimation.
  • To address the challenge of achieving both accurate synchronization and reliable parameter selection.
  • To investigate the effectiveness of these methods in systems exhibiting deterministic chaos.

Main Methods:

  • Introduced a 'balanced' synchronization method incorporating conditional Lyapunov exponents.

Related Experiment Videos

  • Developed a time-varying coupling method based on synchronization error.
  • Explored systems with deterministic chaos, including those with noisy data signals.

    • Main Results:

    • The proposed methods effectively balance synchronization cost and parameter determination accuracy.
    • The time-varying coupling method demonstrates generalized synchronization in well-determined parameter regions.
    • Successful parameter estimation was achieved even in the presence of noise.

    Conclusions:

    • The novel balanced synchronization techniques enhance the reliability of parameter estimation from observational data.
    • These methods offer a robust approach for model completion in complex dynamical systems.
    • The findings are applicable to systems with deterministic chaos and noisy data.