TY - GEN
T1 - The leaky Least Mean Fourth adaptive algorithm
AU - Khattak, Obaid Ur Rehman
AU - Zerguine, Azzedine
PY - 2010
Y1 - 2010
N2 - In this work, a leakage-based variant of the Least Mean Fourth (LMF) algorithm, the leaky Least Mean Fourth (LLMF) algorithm, has been derived which helps mitigate the weight drift problem experienced in the conventional LMF algorithm. The main aim of this work is to derive the LLMF adaptive algorithm and conduct transient analysis using the energy conservation relation framework. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and show improved performance obtained through the use of LLMF over the conventional LMF algorithm in a weight drift environment.
AB - In this work, a leakage-based variant of the Least Mean Fourth (LMF) algorithm, the leaky Least Mean Fourth (LLMF) algorithm, has been derived which helps mitigate the weight drift problem experienced in the conventional LMF algorithm. The main aim of this work is to derive the LLMF adaptive algorithm and conduct transient analysis using the energy conservation relation framework. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and show improved performance obtained through the use of LLMF over the conventional LMF algorithm in a weight drift environment.
KW - Adaptive filters
KW - Leaky Least Mean Fourth (LLMF)
KW - Weight drift
UR - https://www.scopus.com/pages/publications/78650297818
U2 - 10.1109/ISSPA.2010.5605591
DO - 10.1109/ISSPA.2010.5605591
M3 - Conference contribution
AN - SCOPUS:78650297818
SN - 9781424471676
T3 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
SP - 546
EP - 549
BT - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
ER -