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Related Experiment Videos

A robust time-varying identification algorithm using basis functions.

Rui Zou1, Hengliang Wang, Ki H Chon

  • 1Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794-8181, USA.

Annals of Biomedical Engineering
|September 16, 2003
PubMed
Summary
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A new optimal parameter search algorithm (OPS) enhances time-varying (TV) autoregressive (AR) and autoregressive moving average (ARMA) model identification. This method accurately estimates TV parameters and model orders, outperforming existing techniques in simulations and real-world applications.

Area of Science:

  • Signal processing
  • Time-series analysis
  • System identification

Background:

  • Accurate identification of time-varying (TV) models is crucial in dynamic systems analysis.
  • Existing methods for TV autoregressive (AR) and autoregressive moving average (ARMA) models have limitations in parameter estimation and order selection.
  • Time-invariant (TIV) model order search criteria are effective but not directly applicable to TV models.

Purpose of the Study:

  • To extend the optimal parameter search algorithm (OPS), a time-invariant (TIV) model order search criterion, for the identification of time-varying (TV) AR and ARMA models.
  • To develop an algorithm that can accurately estimate TV parameters and determine model orders for AR and ARMA systems.
  • To improve the performance and reduce computational complexity in TV model identification.

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Main Methods:

  • The proposed method transforms TV coefficients into TIV parameters by expanding them onto a finite set of basis sequences.
  • Leverages the optimal parameter search algorithm (OPS) for accurate model order selection and identification of significant model terms.
  • Incorporates a mechanism to discriminate and remove insignificant basis sequences, reducing the number of parameters to estimate.

Main Results:

  • The extended OPS algorithm accurately estimates TV AR or ARMA models and determines their orders.
  • Computer simulations demonstrate superior tracking of TV parameters compared to the recursive least squares method for AR models.
  • Application to renal blood flow signals shows higher-resolution time-varying spectral capability than the short-time Fourier transform (STFT), with fewer spurious peaks.

Conclusions:

  • The developed algorithm effectively identifies time-varying AR and ARMA models by converting them into a TIV framework.
  • The method offers enhanced accuracy and efficiency in parameter estimation and model order selection for dynamic systems.
  • The algorithm provides superior spectral analysis capabilities for real-world physiological signals compared to traditional methods like STFT.