An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

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 …

Using the generalized Gaussian probability density function as the kernel, a generalized
correntropy has been proposed. A generalized maximum correntropy criterion (GMCC) …

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 …

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 …

In this paper, a new framework of cost function for designing robust adaptive filtering
algorithms is developed. This new cost framework, called sigmoid cost function, results from …

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 …

This paper presents a new convergence analysis of the least mean fourth (LMF) adaptive
algorithm, in the mean square sense. The analysis improves previous results, in that it is …

The maximum Versoria criterion algorithm (MVC) exhibits lower steady-state misalignment
and less complexity as compared to the maximum correntropy criterion (MCC) algorithm in …