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Related Experiment Videos

Fast, robust identification of nonlinear physiological systems using an implicit basis expansion.

D T Westwick1, K R Lutchen

  • 1Department of Electrical and Computer Engineering, University of Calgary, Alberta, Canada. westwick@enel.ucalgary.ca

Annals of Biomedical Engineering
|December 29, 2000
PubMed
Summary

This study introduces a novel method combining fast orthogonalization with arbitrary basis expansions for Volterra series modeling. This approach efficiently reduces computational demands and storage requirements for nonlinear system identification.

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Area of Science:

  • System identification
  • Nonlinear dynamics
  • Signal processing

Background:

  • Volterra series models are powerful for nonlinear systems but computationally intensive.
  • Parameter explosion limits their application to low-order, short-memory systems.
  • Existing methods include fast orthogonal algorithms and basis expansions (e.g., Laguerre) to mitigate complexity.

Purpose of the Study:

  • To develop an efficient method for identifying Volterra series models.
  • To combine fast orthogonalization techniques with arbitrary basis expansions.
  • To reduce computational and storage requirements for nonlinear system identification.

Main Methods:

  • The proposed method integrates fast orthogonalization with arbitrary basis expansions.
  • Demonstrates independent application of orthogonalization and basis expansion.

Related Experiment Videos

  • Utilizes simulations on a nonlinear auditory processing model for validation.

    • Main Results:

    • The combined approach effectively reduces model parameters and estimation complexity.
    • Orthogonalization and expansion can be performed independently, allowing flexible generation of multiple basis expansions.
    • Simulations confirm the equivalence of kernels estimated via direct and the proposed implicit basis expansion techniques.
    • The new algorithm shows competitive running times compared to existing methods.

    Conclusions:

    • The proposed method offers an efficient and flexible solution for identifying Volterra series models.
    • It successfully addresses the computational and storage challenges associated with high-order, long-memory nonlinear systems.
    • This technique enhances the applicability of Volterra series in fields like nonlinear system analysis and signal processing.