This letter fortifies the recently introduced hierarchical-optimization recursive least squares
(HO-RLS) against outliers which contaminate infrequently linear-regression models. Outliers …

This brief proposes a new approach for deriving a robust saturation algorithm against
impulsive noises. The proposed approach presents the saturation algorithm by using the …

Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to
adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts …

Resistant training in radial basis function (RBF) networks is the topic of this paper. In this
paper, one modification of Gauss-Newton training algorithm based on the theory of robust …

In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been
extensively applied in distributed parameter estimation and target tracking of multitask …

Two robust quasi-Newton (QN) adaptive filtering algorithms that perform well in impulsive-
noise environments are proposed. The new algorithms use an improved estimate of the …

This paper proposes a new variable step‐size sign algorithm through the minimisation of
mean‐square deviation (MSD). As it is difficult to obtain the MSD accurately, the upper …

Exponential growth in data volumes has promoted widespread interest in data-selective
adaptive algorithms. In a pioneering work, Diniz developed the data-selective least mean …

This work provides novel robust and regularized algorithms for parameter estimation with
applications in vehicle tractive force prediction and mass estimation. Given a large record of …

The generalized variable step-size diffusion continuous mixed p-norm (GVSS-DCMPN)
algorithm is proposed in this paper, which is derived based on the improved continuous …