TY - GEN
T1 - A normalized least mean fourth algorithm with improved stability
AU - Eweda, Eweda
AU - Zerguine, Azzedine
PY - 2010
Y1 - 2010
N2 - The paper presents a new normalized least mean fourth (NLMF) algorithm. The algorithm is derived through the minimization of the mean fourth normalized estimation error. The main advantage of the algorithm with respect to the available NLMF algorithms is that it remains stable as the input power of the adaptive filter increases. A stability step size bound of the proposed algorithm is derived. The step size bound depends on the weight initialization, while it does not depend on the input power of the adaptive filter. Simulation results support the analytical results of the paper.
AB - The paper presents a new normalized least mean fourth (NLMF) algorithm. The algorithm is derived through the minimization of the mean fourth normalized estimation error. The main advantage of the algorithm with respect to the available NLMF algorithms is that it remains stable as the input power of the adaptive filter increases. A stability step size bound of the proposed algorithm is derived. The step size bound depends on the weight initialization, while it does not depend on the input power of the adaptive filter. Simulation results support the analytical results of the paper.
KW - Least mean fourth (LMF) algorithm
KW - Normalized least mean fourth (NLMF) algorithm
UR - https://www.scopus.com/pages/publications/79958015984
U2 - 10.1109/ACSSC.2010.5757551
DO - 10.1109/ACSSC.2010.5757551
M3 - Conference contribution
AN - SCOPUS:79958015984
SN - 9781424497218
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1002
EP - 1005
BT - Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
ER -