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Related Experiment Videos

Stochastic error whitening algorithm for linear filter estimation with noisy data.

Yadunandana N Rao1, Deniz Erdogmus, Geetha Y Rao

  • 1Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|July 10, 2003
PubMed
Summary

This study introduces a new stochastic gradient algorithm using the error whitening criterion (EWC) to improve linear filter estimation with noisy data. The novel algorithm offers unbiased results, unlike traditional mean squared error methods.

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Area of Science:

  • Signal Processing
  • Control Systems Engineering
  • Machine Learning

Background:

  • Mean Squared Error (MSE) is standard for linear filter estimation but yields biased results with noisy signals.
  • Additive white disturbances in input signals pose a significant challenge for traditional estimation methods.

Purpose of the Study:

  • To present a novel stochastic gradient algorithm for linear filter estimation.
  • To address the bias issue in MSE caused by noisy input signals.
  • To introduce a new algorithm based on the error whitening criterion (EWC).

Main Methods:

  • Derivation of an online stochastic gradient algorithm based on the error whitening criterion (EWC).
  • Theoretical convergence proof of the algorithm under mild assumptions.

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  • Development of extensions for faster, step-size independent convergence.
  • Main Results:

    • The proposed stochastic EWC algorithm provides unbiased estimates in the presence of additive white disturbances.
    • Simulations demonstrate superior performance compared to MSE and total-least squares in parameter estimation.
    • The algorithm shows robustness and potential for practical applications.

    Conclusions:

    • The novel stochastic EWC algorithm effectively overcomes the limitations of MSE in noisy environments.
    • The algorithm offers improved accuracy and convergence properties for linear filter estimation.
    • Potential applications include designing robust inverse controllers for systems with noisy data.