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

The interpretation of kernels--an overview.

G K Hung1, L W Stark

  • 1Dept. of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854.

Annals of Biomedical Engineering
|January 1, 1991
PubMed
Summary

The kernel identification method models nonlinear systems. Examining kernel shapes reveals system structure and predicts dynamic behavior in physiological systems.

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

Spontaneous eye movements during visual imagery reflect the content of the visual scene.

Journal of cognitive neuroscience·2013
Same author

Simulation studies of descending and reflex control of fast movements.

Journal of motor behavior·2013
Same author

Top-down guided eye movements.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008
Same author

Effect of cumulative nearwork on accommodative facility and asthenopia.

International ophthalmology·2003
Same author

Differential retinal-defocus magnitude during eye growth provides the appropriate direction signal.

Medical science monitor : international medical journal of experimental and clinical research·2001
Same author

A unifying theory of refractive error development.

Bulletin of mathematical biology·2000

Area of Science:

  • Systems Engineering
  • Nonlinear Dynamics
  • Physiological Modeling

Background:

  • The kernel identification method is a key technique for representing nonlinear system dynamics.
  • Its application spans diverse physical and physiological domains.
  • Interpreting higher-degree kernel shapes has significantly advanced its utility.

Purpose of the Study:

  • To demonstrate how kernel shapes reveal internal system structure.
  • To illustrate the relationship between model parameters and kernel shapes.
  • To showcase the predictive power of kernel shape analysis in physiological systems.

Main Methods:

  • Utilizing nonlinear models with known structures to generate characteristic kernel shapes.
  • Analyzing variations in model parameters and their impact on kernel morphology.
  • Applying kernel identification to physiological system data.

Main Results:

  • A repertoire of kernel shapes corresponding to different nonlinear model structures was established.
  • Parameter variations in models produced distinct and predictable changes in kernel shapes.
  • Kernel shape analysis from physiological data enabled accurate dynamic behavior predictions.

Conclusions:

  • Kernel shape examination is a powerful tool for understanding and predicting the dynamic behavior of nonlinear and physiological systems.
  • The method's effectiveness is demonstrated through examples of known nonlinear models and physiological applications.
  • Understanding the limitations of the kernel identification method's applicable range is crucial for its appropriate use.

Related Experiment Videos