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The identification of nonlinear biological systems: Volterra kernel approaches
1Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada.
Annals of Biomedical Engineering
|March 1, 1996
Summary
This study explores representing nonlinear biomedical systems using Volterra series. It details a kernel estimation technique for system identification, effective even with noisy data.
Area of Science:
- Biomedical Engineering
- Systems Biology
- Nonlinear Dynamics
Background:
- Nonlinear biomedical systems present challenges in accurate modeling and identification.
- Volterra series offer a powerful framework for approximating nonlinear system behavior.
Purpose of the Study:
- To investigate the conditions for representing nonlinear biomedical systems using Volterra series.
- To examine system identification via kernel estimation within Volterra functional expansions.
- To evaluate a kernel estimation technique's performance and biological applications.
Main Methods:
- Analysis of conditions for Volterra series approximation of nonlinear systems.
- Kernel estimation technique for identifying system parameters.
- Demonstration on simulated data (clean and noisy) and biological system cascades.
- Evaluation of computational running time for the estimation technique.
Main Results:
- The study details the applicability of Volterra series for nonlinear system representation.
- A specific kernel estimation technique is shown to be effective and efficient.
- The technique successfully identified both single-input single-output and multivariable system cascades.
Conclusions:
- Volterra series provide a viable method for nonlinear biomedical system modeling.
- Kernel estimation is a robust technique for system identification in biomedical contexts.
- The investigated method demonstrates practical utility in complex biological system analysis.
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