This paper focuses on the parameter estimation issues of multivariate equation‐error
autoregressive moving average systems. By applying the gradient search and the multi …

In this paper, three Lorentzian based robust adaptive algorithms are proposed for identifying
systems in presence of impulsive noise. The first algorithm called Lorentzian adaptive …

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

In this paper, Lorentzian norm based adaptive filter (LAF) is implemented to control voltage
source converter (VSC) for improvement in power quality of three phase four wire wind …

This paper presents a family of robust set-membership normalized subband adaptive
filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust …

Sparse recursive least squares (RLS) adaptive filter algorithms achieve faster convergence
and better performance than the standard RLS algorithm under sparse systems. Existing …

In practical applications, the impulse responses (IRs) of some network echo paths are
blocksparse (BS), while the traditional proportionate and zero attraction algorithms do not …

This paper proposes variable step-size-norm constraint normalized least mean square (-
NLMS) algorithms for sparse channel identification. The mean square deviation of the-NLMS …

The proportionate normalized least mean square (PNLMS) algorithm and its variants are by
far the most popular adaptive filters that are used to identify sparse systems. The …

The proportionate updating (PU) mechanism has been widely adopted in least mean
squares (LMS) adaptive filtering algorithms to exploit the system sparsity. In this brief, we …