On the convergence, steady-state, and tracking analysis of the SRLMMN algorithm. Faiz, M. M. U. & Zerguine, A. In *2015 23rd European Signal Processing Conference (EUSIPCO)*, pages 2691-2695, Aug, 2015. Paper doi abstract bibtex In this work, a novel algorithm named sign regressor least mean mixed-norm (SRLMMN) algorithm is proposed as an alternative to the well-known least mean mixed-norm (LMMN) algorithm. The SRLMMN algorithm is a hybrid version of the sign regressor least mean square (SRLMS) and sign regressor least mean fourth (SRLMF) algorithms. Analytical expressions are derived to describe the convergence, steady-state, and tracking behavior of the proposed SRLMMN algorithm. To validate our theoretical findings, a system identification problem is considered for this purpose. It is shown that there is a very close correspondence between theory and simulation. Finally, it is also shown that the SRLMMN algorithm is robust enough in tracking the variations in the channel.

@InProceedings{7362873,
author = {M. M. U. Faiz and A. Zerguine},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {On the convergence, steady-state, and tracking analysis of the SRLMMN algorithm},
year = {2015},
pages = {2691-2695},
abstract = {In this work, a novel algorithm named sign regressor least mean mixed-norm (SRLMMN) algorithm is proposed as an alternative to the well-known least mean mixed-norm (LMMN) algorithm. The SRLMMN algorithm is a hybrid version of the sign regressor least mean square (SRLMS) and sign regressor least mean fourth (SRLMF) algorithms. Analytical expressions are derived to describe the convergence, steady-state, and tracking behavior of the proposed SRLMMN algorithm. To validate our theoretical findings, a system identification problem is considered for this purpose. It is shown that there is a very close correspondence between theory and simulation. Finally, it is also shown that the SRLMMN algorithm is robust enough in tracking the variations in the channel.},
keywords = {adaptive filters;convergence of numerical methods;least mean squares methods;regression analysis;tracking;SRLMMN algorithm convergence;steady-state analysis;tracking analysis;sign regressor least mean mixed norm algorithm;sign regressor least mean square algorithm;hybrid SRLMS-SRLMF algorithm;sign regressor least mean fourth algorithm;Signal processing algorithms;Steady-state;Algorithm design and analysis;Convergence;Mathematical model;Europe;Signal processing;LMS;LMF;LMMN;SRLMS;SRLMF;SRLMMN;sign regressor;mixed-norm;convergence;steady-state;tracking},
doi = {10.1109/EUSIPCO.2015.7362873},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104791.pdf},
}