Convergence analysis of the variable weight mixed-norm LMS-LMF adaptive algorithm

TitleConvergence analysis of the variable weight mixed-norm LMS-LMF adaptive algorithm
Publication TypeConference Paper
Year of Publication2000
AuthorsZerguine, A., and T. Aboulnasr
Conference NameSignals, Systems and Computers, 2000. Conference Record of the 34th Asilomar Conference on
Pagination279 -282 vol.1
Keywordsadaptive filters, convergence analysis, convergence of numerical methods, cost functions, filtering theory, least mean squares methods, minimisation, objective function, step size, sufficient and necessary conditions, time varying weighting factor, time-varying filters, variable weight mixed-norm LMS-LMF adaptive algorithm, weighted sum
Abstract

In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares-least mean fourth) adaptive algorithm is derived. The proposed algorithm minimizes an objective function defined as a weighted sum of the LMS and LMF cost functions where the weighting factor is time varying and adapts itself so as to allow the algorithm to keep track of the variations in the environment. Sufficient and necessary conditions for the convergence of the algorithm are derived. Furthermore, bounds on the step size to ensure convergence of the LMF algorithm are also derived

URLhttp://dx.doi.org/10.1109/ACSSC.2000.910959
DOI10.1109/ACSSC.2000.910959

a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949
Email:

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2020 The University of British Columbia