Stochastic Approximation with Smoothing for Optimization of an Adaptive Recursive Filter

  • W. Edmonson
  • K. Srinivasan
  • C. Wang
  • J. Principe
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 7)


A major concern of adaptive IIR filter is that with the objective function being non- convex, currently used gradient methods have a tendency to converge to the local minimum. The stochastic approximation with convolution smoothing represents a simple approach for deriving a global optimization algorithm for adaptive filtering. This stochastic approximation method has been derived for adaptive system identification. Optimization is based on minimizing the mean square error objective function. The mean square error is a function of time series data that is statistically varying. An experimental result demonstrates the viability of using stochastic approximation for adaptive filtering.


stochastic approximation digital signal processing adaptive IIR filtering system identification global optimization 


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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • W. Edmonson
    • 1
  • K. Srinivasan
    • 1
  • C. Wang
    • 1
  • J. Principe
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of FloridaGainesvilleUSA

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