This paper introduces a new class of nonlinear adaptive filters, whose structure is based on
Hammerstein model. Such filters derive from the functional link adaptive filter (FLAF) model …

Adaptive filters are at the core of many signal processing applications, ranging from acoustic
noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to …

The combination of filters concept is a simple and flexible method to circumvent various
compromises hampering the operation of adaptive linear filters. Recently, applications which …

Adaptive filtering schemes exhibit a compromise between convergence speed and steady-
state mean square error. Trying to overcome this trade-off, convex combination of adaptive …

Diffusion adaptation is a powerful strategy for distributed estimation and learning over
networks. Motivated by the concept of combining adaptive filters, this work proposes a …

Both convex and affine combinations are highly effective for distributed adaptive networks,
enabling these networks to create new diffusion strategies by combining the strengths of …

Adaptive networks (ANs) are effective real time techniques to process and track events
observed by sensor networks and, more recently, to equip Internet of Things (IoT) …

In this paper, we propose an approach to the transient and steady-state analysis of the affine
combination of one fast and one slow adaptive filters. The theoretical models are based on …

Combinations of adaptive filters have attracted attention as a simple solution to improve filter
performance, including tracking properties. In this paper, we consider combinations of LMS …

A method for adaptively minimizing the lp norm relying on the convex combination of two
recursive least p-norm (RLpN) filters is presented. The approach is of interest when the …