Performance Comparisons

  • Bernard Mulgrew
  • Colin F. N. Cowan
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 56)

Abstract

While logistics preclude a comparison of the complete set of algorithms that have been mentioned in chapter 2, it is possible to examine the performance of a subset whose elements are representative of the three classes into which adaptive filters may be divided. These three classes are: (i) stochastic gradient search algorithms such as the LMS algorithm of subsection 2.5.1 and the BLMS algorithm of subsection 2.5.2, (ii) self-orthogonalising or transform domain algorithms such as the sliding DFT structure of subsection 2.6.1, and (iii) least squares techniques such as the simple RLS algorithm of subsection 2.4.1. The convergence performance of one or two algorithms from each class will be studied by computer simulation.

Keywords

Covariance Autocorrelation Deconvolution 

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Copyright information

© Kluwer Academic Publishers 1988

Authors and Affiliations

  • Bernard Mulgrew
    • 1
  • Colin F. N. Cowan
    • 1
  1. 1.University of EdinburghUK

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