As a robust nonlinear similarity measure in kernel space, correntropy has received
increasing attention in domains of machine learning and signal processing. In particular, the …

In recent years, correntropy-based algorithms which include maximum correntropy criterion
(MCC), generalized MCC (GMCC), kernel MCC (KMCC) and hyperbolic cosine function …

With the promises of smart grids, power can be more efficiently and reliably generated,
transmitted, and consumed over conventional electricity systems. Through the two-way flow …

In this paper, a least lncosh (Llncosh) algorithm is derived by utilizing the lncosh cost
function. The lncosh cost is characterized by the natural logarithm of hyperbolic cosine …

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic
cost inspired by the “competitive methods” from the online learning literature. The …

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy
of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine …

The combination of the famed kernel trick and affine projection algorithms (APAs) yields
powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up …

We tackle the problem of channel estimation in environments that exhibit both sparse, time-
varying impulse responses and impulsive noise with Symmetric alpha-Stable (SαS) …

In this brief, an affine-projection-like M-estimate (APLM) algorithm is proposed for robust
adaptive filtering. To eliminate the adverse effects of impulsive noise in case of the impulse …

Adopting mean-square error (MSE) criterion, distributed estimation algorithms achieve
desirable performance if the background noise is drawn from the Gaussian distribution …