Performance of LMS-Newton adaptation algorithms with variable convergence factor in nonstationary environments

  • Authors:

  • Marcello L. R. de Campos;Paulo S. R. Diniz;Andreas Antoniou

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Victoria, Victoria, B.C., Canada;Programa de Engenharia Elétrica e Dept. de Eletrônica, COPPE, EE, Federal University of Rio de Janeiro, R.J., Brazil;Department of Electrical and Computer Engineering, University of Victoria, Victoria, B.C., Canada

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
  • Year:
  • 1993

Quantified Score

Hi-index 0.00

Visualization

Abstract

An analysis of two LMS-Newton adaptive filtering algorithms with variable convergence factor is presented. The relations of these algorithms with the conventional recursive least-squares algorithm is first addressed. Their performance in stationary and nonstationary environments is then studied and closed-form formulas for the excess of mean-square error (MSE) are derived. The paper concludes with experimental results that demonstrate the validity of the analysis presented.