In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering
algorithms. We express the RLS problem in terms of auxiliary normal equations with respect …

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo
cancellation in vehicle hands-free communication systems and video teleconferencing …

A proportionate normalized least mean square (PNLMS) algorithm has been proposed for
sparse impulse response identification. It provides fast initial convergence, but it begins to …

By analyzing the coefficient adaptation process of the steepest descent algorithm, the
condition under which the fastest overall convergence will be achieved is obtained and the …

Distributed estimation of a common unknown parameter vector can be realized efficiently
and robustly over an adaptive network employing diffusion strategies. In the adapt-then …

While standard approaches for acoustic echo cancellation in telecommunication systems
assume that the echo path to be identified can be modeled by a linear system, in practice …

Partial-update adaptive signal processing algorithms not only permit significant complexity
reduction in adaptive filter implementations, but can also improve adaptive filter performance …

Discrete‐time Volterra models are widely used in various application areas. Their
usefulness is mainly because of their ability to approximate to an arbitrary precision any …

In this paper, we present mean-squared convergence analysis for the partial-update
normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the …

The sequential partial-update least mean square (S-LMS)-based algorithms are efficient
methods for reducing the arithmetic complexity in adaptive system identification and other …