Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …

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 …

The optimality of second-order statistics depends heavily on the assumption of Gaussianity.
In this paper, we elucidate further the probabilistic and geometric meaning of the recently …

To date, most linear and nonlinear Kalman filters (KFs) have been developed under the
Gaussian assumption and the well-known minimum mean square error (MMSE) criterion. In …

The applications of error entropy (EE) are sometimes limited because its shape cannot be
flexibly adjusted by the default Gaussian kernel function to adapt to noise variation and thus …

A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented
to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) …

Correntropy has been recently defined as a localised similarity measure between two
random variables, exploiting higher order moments of the data. This paper presents the use …

The design of echo state network (ESN) parameters relies on the selection of the maximum
eigenvalue of the linearized system around zero (spectral radius). However, this procedure …

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract
higher order statistics of data and offer potentially significant performance improvement over …

This paper reports new results in adopting entropy concepts to the training of neural
networks to perform wind power prediction as a function of wind characteristics (speed and …