Paper data
Title:
A TimeVarying Normalized MixedNorm LMSLMF Algorithm Author(s): Zerguine Azzedine, KFUPM Page numbers in the proceedings: Volume I pp 337340 Session: Nonlinear Signal and Systems / Adaptive Methods
Paper abstract
The normalized least mean square (NLMS) algorithm is known to result in a faster convergence than the least mean square (LMS) algorithm but at the expense of a larger steadystate error. A timevarying normalized mixednorm LMSleast mean fourth (LMF) algorithm is presented in this work to preserve the fast convergence of the NLMS algorithm while resulting in a lower steadystate error. The simulation results show that a substantial improvement, in both convergence time and steady state error, can be obtained with this mixednorm algorithm.
Paper
