Proportionate Maximum Versoria Criterion (P-MVC) based adaptive algorithms for unknown
sparse system identification problem are proposed in this brief. The conventional …

In this work we consider a scenario in which several dispersed nodes intend to identify a
nonlinear Volterra system. Such system is represented by a series that has sparse kernels …

A novel zero attraction affine projection sign algorithm (ZA-APSA) for strong impulsive and
sparse environment is proposed in this paper. Here l _ 1 l 1 norm penalty is introduced to …

There are two main families of algorithms that tackle the problem of sparse system
identification: the proportionate family and the one that employs sparsity-promoting penalty …

In this paper, we propose two novel p-norm penalty least mean square (ℓ p-LMS) algorithms
as supplements of the conventional ℓ p-LMS algorithm established for sparse adaptive …

Distributed inference tasks could be performed by adaptive filtering techniques. Several
enhancement strategies for such techniques were proposed, such as sparsity-aware …

This paper proposes a new approach to identify time varying sparse systems. The proposed
approach uses Zero‐Attracting Least Mean Square (ZA‐LMS) algorithm with an adaptive …

The zero attracting least mean square algorithm has improved performance than
conventional LMS when the system is sparse and its performance decreases when the …

In this paper, we propose sparsity-aware data-selective adaptive filtering algorithms with
adjustable penalties. Prior work incorporates a penalty function into the cost function used in …

Adaptive filtering algorithms implement an estimation of a set of parameters. Frequently, the
system to be identified is sparse, in the sense that most of its energy is concentrated among …