CONVERGENCE MODELS FOR ADAPTIVE GRADIENT AND LEAST SQUARES ALGORITHMS.

Michael L. Honig*, David G. Messerschmitt

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

A simple model characterizing the convergence properties of an adaptive digital lattice filter using gradient algorithms has been reported. This model is extended to the least mean square (LMS) lattice joint process estimator, to the recursive least squares (LS) algorithms, and is compared with computer simulations. Interestingly, the LS models are more accurate than the previous LMS models. In addition, although the LS lattice consistently converges somewhat faster than the LMS lattice, they both exhibit similar behavior.

Original languageEnglish (US)
Pages (from-to)267-270
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
StatePublished - Jan 1 1981
EventUnknown conference - Atlanta, Ga
Duration: Mar 30 1981Apr 1 1981

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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