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Two methods for identifying Wiener cascades having noninvertible static nonlinearities.

M J Korenberg1, I W Hunter

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

Annals of Biomedical Engineering
|January 7, 2000
PubMed
Summary
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New methods identify Wiener cascade components, overcoming limitations of previous techniques. These approaches ensure stability and work even with non-invertible nonlinearities, improving dynamic system analysis.

Area of Science:

  • * Systems Neuroscience
  • * Signal Processing
  • * Computational Biology

Background:

  • * Wiener cascade identification is crucial for understanding dynamic systems.
  • * Previous methods by Hunter & Korenberg and Paulin have limitations regarding invertibility and stability.
  • * Paulin's modification enabled nonmonotonic nonlinearities but used potentially unstable ARMA models.

Purpose of the Study:

  • * To propose two novel, stable methods for identifying Wiener cascade components (linear and nonlinear elements).
  • * To overcome limitations of existing methods, particularly concerning static nonlinearity invertibility and model stability.
  • * To provide robust identification techniques applicable to complex dynamic systems.

Main Methods:

  • * Recasting Paulin's procedure using an impulse response representation for the dynamic linear element.

Related Experiment Videos

  • * Introducing two key innovations to improve convergence properties and robustness.
  • * Testing methods on challenging examples with polynomial nonlinearities, high-pass linear elements, and noise.

    • Main Results:

    • * Developed two effective methods for Wiener cascade identification.
    • * Successfully identified systems with non-invertible and non-monotonic static nonlinearities.
    • * Demonstrated robustness against high-degree polynomial nonlinearities and significant output noise (50%).

    Conclusions:

    • * The proposed methods offer stable and effective Wiener cascade identification.
    • * These techniques are versatile, handling non-invertible nonlinearities and complex linear dynamics.
    • * The findings advance the analysis of dynamic systems in neuroscience and engineering.