The conventional logarithm cost-based adaptive filters, eg, the least mean logarithmic
square (LMLS) algorithm, cannot combat impulsive noises at the filtering process. To …

The least mean fourth algorithm has several stability problems. Its stability depends on the
variance and distribution type of the adaptive filter input, the noise variance, and the …

The least mean fourth (LMF) algorithm has several stability problems. Its stability depends
on the variance and distribution type of the adaptive filter input, the noise variance, and the …

As one of adaptive filtering algorithms based on the high order error power (HOEP) criterion,
the least mean absolute third (LMAT) algorithm outperforms the least mean square (LMS) …

This paper addresses the stability issues of the least mean absolute third (LMAT) algorithm
using the normalization based on the third order in the estimation error. A novel robust …

The paper is concerned with stabilizing the family of adaptive filtering algorithms based on
minimizing the 2Lth moment of the estimation error, with L being an integer greater than 1 …

The paper investigates a new stability problem of the least mean fourth (LMF) algorithm,
which is the dependence of the algorithm stability on the time-variation of the target weights …

Recently, it has been shown that the stability of the least mean fourth (LMF) algorithm
depends on the nonstationarity of the plant. The present paper investigates the possibility of …

This paper studies the stochastic behavior of the least mean fourth (LMF) algorithm for a
system identification framework when the input signal is a non-stationary white Gaussian …

The stability problems of the least mean fourth (LMF) algorithm put a limitation on its tracking
capability. The paper investigates the possibility of solving this problem via stabilization of …