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Related Experiment Videos

Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre

K H Chon1, R J Cohen, N H Holstein-Rathlou

  • 1Brown University, Department of Molecular Pharmacology, Physiology and Biotechnology, Providence, RI 02912, USA. kchon@medal.biomed.brown.edu

Annals of Biomedical Engineering
|July 1, 1997
PubMed
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A new algorithm models time series data using linear and nonlinear autoregressive moving average (ARMA) models. This approach improves upon Laguerre expansion of kernels (LEK) by offering a more compact system representation and easier physiological interpretation.

Area of Science:

  • * Time series analysis
  • * System identification
  • * Signal processing

Background:

  • * Traditional methods like Volterra-Wiener analysis estimate system dynamics using moving average models.
  • * Laguerre expansion of kernels (LEK) is a technique for estimating Volterra-Wiener kernels.
  • * LEK has limitations in system representation compactness and physiological interpretation of higher-order kernels.

Purpose of the Study:

  • * To develop a novel linear and nonlinear autoregressive moving average (ARMA) identification algorithm for time series modeling.
  • * To extend the Laguerre expansion of kernels (LEK) approach by incorporating past output values.
  • * To achieve a more compact system representation and facilitate easier physiological interpretation of higher-order kernels compared to existing methods.

Main Methods:

Keywords:
Non-programmatic

Related Experiment Videos

  • * Development of a linear and nonlinear ARMA identification algorithm.
  • * Adaptation of the Laguerre expansion of kernels (LEK) framework.
  • * Integration of past output values into the ARMA model structure.

Main Results:

  • * The proposed ARMA model-based approach offers a significant reduction in the number of required Laguerre functions compared to the Volterra-Wiener approach.
  • * This leads to a more compact system representation.
  • * Simulation results indicate superior performance in estimating system dynamics compared to LEK in specific scenarios, even with substantial additive measurement noise.

Conclusions:

  • * The proposed ARMA identification algorithm effectively models linear and nonlinear time series data.
  • * The method enhances system representation compactness and simplifies the physiological interpretation of higher-order kernels.
  • * The algorithm demonstrates robust performance and effectiveness, particularly in noisy environments.