Title :
Convergence behavior of the normalized least mean fourth algorithm
Author :
Zerguine, Azzedine
Author_Institution :
Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
fDate :
Oct. 29 2000-Nov. 1 2000
Abstract :
The normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. Sufficient conditions for the NLMF algorithm convergence in the mean are obtained and the analysis of the steady-state performance is carried out using the feedback approach. Simulation results confirm the performance of the NLMF algorithm.
Keywords :
convergence of numerical methods; correlation methods; feedback; least mean squares methods; statistical analysis; LMF algorithm; NLMF algorithm; NLMS algorithm; convergence behavior; feedback approach; input data correlation statistics; normalized least mean fourth algorithm; performance; simulation results; steady-state performance; sufficient conditions; Adaptive filters; Algorithm design and analysis; Convergence; Eigenvalues and eigenfunctions; Feedback; Least squares approximation; Minerals; Petroleum; Statistics; Steady-state;
Conference_Titel :
Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-6514-3
DOI :
10.1109/ACSSC.2000.910958
