A FAMILY OF NORMALIZED LEAST MEAN FOURTH ALGORITHMS

In this work, a family of normalized least mean fourth algo-rithms is presented. Unlike the LMF algorithm, the conver-gence behavior of these algorithms is independent of the in-put data correlation statistics. The first proposed algorithm uses a simple normalization of the regressor and is called simply the NLMF. The second algorithm consists of a mixed normalized LMF (XE-NLMF) algorithm which is normal-ized by the mixed signal and error powers. Finally, the third algorithm, called the variable XE-NLMF, is a modified ver-sion of the XE-NLMF where the mixed-power parameter is time-varying. An enhancement in performance is obtained through the use of these techniques over the LMF algorithm. Moreover, the simulation results obtained confirm the theo-retical predictions on the performance of these normalized LMF algorithms. 1