LMS-Based Algorithms

  • Paulo Sergio Ramirez
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 694)

Abstract

There are a number of algorithms for adaptive filters which are derived from the conventional LMS algorithm discussed in the previous chapter. The objective of the alternative LMS-based algorithms is either to reduce computational complexity or convergence time. In this chapter, several LMS-based algorithms are presented and analyzed, namely, the quantized-error algorithms [1]–[11], the frequency–domain (or transform-domain) LMS algorithm [12]–[14], the normalized LMS algorithm [15], the LMS-Newton algorithm [16]–[17], and the affine projection algorithm [19]–[24]. Several algorithms that are related to the main algorithms presented in this chapter are also briefly discussed.

Keywords

Input Signal Convergence Speed Adaptive Filter Convergence Factor Affine Projection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Paulo Sergio Ramirez
    • 1
  1. 1.Federal University of Rio de JaneiroRio de JaneiroBrazil

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