Parametric and Nonparametric Nonlinear Modeling of Renal Autoregulation Dynamics

  • Ki H. Chon
  • Niels H. Holstein-Rathlou
  • Donald J. Marsh
  • Vasilis Z. Marmarelis

Abstract

The generality of Volterra-Wiener analysis has led to the broad use of nonparamet-ric models of nonlinear systems. However, compact model representation of physiological systems cannot be achieved with nonparametric methods. Parametric modeling methods (i.e., Nonlinear Auto-Regressive Moving-Average (NARMA) models) may offer the means for parsimonious model representation. This pa-per explores the use of NARMA modeling via Korenberg’s fast orthogonal search algorithm (which allows us to gauge the significance of the candidate difference equation terms) in order to obtain compact parametric models for nonlinear systems. Computer simulations, as well as experimental renal blood pressure and flow data, are used to demonstrate the potential of this approach for efficient parametric modeling and examine its advantages or disadvantages relative to nonparametric modeling using Volterra-Wiener analysis.

Keywords

Renal Blood Flow Kernel Estimate Volterra Model Volterra Kernel Renal Autoregulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Ki H. Chon
    • 1
  • Niels H. Holstein-Rathlou
    • 2
  • Donald J. Marsh
    • 3
  • Vasilis Z. Marmarelis
    • 4
  1. 1.Department of Biomedical EngineeringUniversity of Southern CaliforniaUSA
  2. 2.Department of Medical PhysiologyUniversity of CopenhagenDenmark
  3. 3.School of MedicineBrown UniversityUSA
  4. 4.Departments of Biomedical and Electrical EngineeringUniversity of Southern CaliforniaUSA

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