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

An illuminative example of nonlinear identification.

D Claude1

  • 1Université Paris-Sud (UFR d'Orsay), Laboratoire des Signaux et Systèmes (CNRS-SUPELEC), Plateau de Moulon, 91192 Gif-sur-Yvette, France. Daniel.Claude@lss.supelec.fr

Computers in Biology and Medicine
|February 15, 2001
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

Implementation of a Length Gauge Based on Optical Frequency Domain Reflectometry (OFDR).

Sensors (Basel, Switzerland)·2026
Same author

Newest Methods and Approaches to Enhance the Performance of Optical Frequency-Domain Reflectometers.

Sensors (Basel, Switzerland)·2024
Same author

An Optical Frequency Domain Reflectometer's (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing.

Sensors (Basel, Switzerland)·2024
Same author

Aminoglycoside nephrotoxicity.

Current drug targets. Infectious disorders·2004
Same author

[PK/PD modeling of aminoglycoside nephrotoxicity].

Pathologie-biologie·2003
Same author

Mathematical model of FSH-induced cAMP production in ovarian follicles.

American journal of physiology. Endocrinology and metabolism·2001
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
Same journal

An AI-driven deep learning pipeline for taxonomic classification and biodiversity assessment of deep-sea environmental DNA.

Computers in biology and medicine·2026
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
See all related articles

Identifying parameters in complex nonlinear systems is challenging. Incorporating algebraic or geometric features aids in parameter identification for biological systems with endogenous rhythms.

Area of Science:

  • Systems Biology
  • Nonlinear Dynamics
  • Mathematical Modeling

Background:

  • Parameter identification in nonlinear systems is a complex challenge.
  • Biological systems often exhibit endogenous rhythms, representing complex nonlinear dynamics.
  • Existing methods may struggle with the intricacies of these biological systems.

Purpose of the Study:

  • To investigate novel approaches for parameter identification in nonlinear systems.
  • To explore the utility of incorporating additional system features for improved identification.
  • To apply these methods to a biological system model with an endogenous rhythm.

Main Methods:

  • Studied a specific nonlinear system exhibiting a limit cycle behavior.
  • Integrated algebraic and geometric system features into the parameter identification process.

Related Experiment Videos

  • Validated the approach on a model representing an endogenous biological rhythm.

    • Main Results:

    • Demonstrated that including additional algebraic features significantly enhances parameter identification.
    • Showed the effectiveness of geometric constraints in refining parameter estimation.
    • Successfully identified key parameters in the model of an endogenous biological rhythm.

    Conclusions:

    • Additional algebraic and geometric features are valuable for parameter identification in nonlinear systems.
    • This approach offers a more robust method for studying biological systems with endogenous rhythms.
    • The findings provide a new perspective for modeling and understanding biological oscillations.