TY - GEN
T1 - The signed regressor least mean fourth (SRLMF) adaptive algorithm
AU - Faiz, Mohammed Mujahid Ulla
AU - Zerguine, Azzedine
AU - Zidouri, Abdelmalek
PY - 2010
Y1 - 2010
N2 - In this work, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. Moreover, the tracking analysis of the proposed algorithm is also provided in a nonstationary environment. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulation results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm.
AB - In this work, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. Moreover, the tracking analysis of the proposed algorithm is also provided in a nonstationary environment. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulation results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm.
UR - https://www.scopus.com/pages/publications/78650273141
U2 - 10.1109/ISSPA.2010.5605532
DO - 10.1109/ISSPA.2010.5605532
M3 - Conference contribution
AN - SCOPUS:78650273141
SN - 9781424471676
T3 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
SP - 333
EP - 336
BT - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
ER -