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Identifying nonlinear difference equation and functional expansion representations: the fast orthogonal algorithm.

M J Korenberg1

  • 1Department of Electrical Engineering, Queen's University, Kingston, Ontario, Canada.

Annals of Biomedical Engineering
|January 1, 1988
PubMed
Summary
This summary is machine-generated.

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A novel orthogonal method efficiently identifies nonlinear system representations, significantly reducing computation time and storage needs for kernel estimation. This approach avoids artifacts common in other techniques, like cross-correlation.

Area of Science:

  • System identification
  • Nonlinear dynamics
  • Computational mathematics

Background:

  • Accurate modeling of nonlinear systems is crucial for understanding complex dynamics.
  • Previous orthogonal methods for system identification can be computationally intensive and require explicit function generation.
  • Cross-correlation techniques may introduce artifacts in kernel measurements.

Purpose of the Study:

  • To present an efficient orthogonal method for identifying functional expansion and difference equation representations of nonlinear systems.
  • To reduce computational time and storage requirements compared to existing orthogonal techniques.
  • To demonstrate the artifact-free nature of the proposed kernel measurement method.

Main Methods:

  • An implicit orthogonal approach is utilized, eliminating the need for explicit orthogonal function creation.

Related Experiment Videos

  • The method accommodates diverse input excitations, including random and deterministic signals.
  • Kernel estimation and difference equation coefficient calculation are performed using the developed technique.

    • Main Results:

    • A 15-fold increase in speed for estimating kernels and difference equation coefficients was achieved.
    • Significantly diminished storage requirements were observed.
    • Simulations using a peripheral auditory system model confirmed artifact-free kernel measurement, outperforming the cross-correlation approach.

    Conclusions:

    • The presented orthogonal method offers a substantial improvement in efficiency for nonlinear system identification.
    • The technique provides accurate kernel measurements free from artifacts.
    • This method is broadly applicable due to its flexibility with various input signal types.