The Extended Kalman Filter (EKF) is a well known tool for the recursive parameter estimation of static and dynamic nonlinear models. In particular, the EKF has been applied to the estimation of the weights of feedforward and recurrent neural network models, i.e. to their training, and shown to be more efficient than recursive and non recursive first-order training algorithms; nevertheless, thes...

This paper proves the effectiveness of Least Mean Square (LMS) estimation algorithm for estimation of power system harmonics, inter-harmonics and sub-harmonics of distorted signal. Accurate estimation can be achieved by this technique for harmonics, sub-harmonics and inter-harmonics estimation including possible variation in the signal such as change in amplitude when applied to a distorted pow...

A recursive algorithm for parameter estimation converging in finite time is introduced. It includes as special case the classical Linear Recursive Square algorithm, and is a generalization of the Super-Twisting Algorithm (STA), a second order sliding mode algorithm, to time-varying systems.

We consider estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. The model considered in the paper is very general as we do not impose any preliminary restrictions on the probabilistic nature of the observation process and cover a wide class of nonlinear recursive procedures. In this paper we study asympt...

In order to gain insights on equalization design in the wireless mobile communication systems, we compare the performance of several multi-channel MLSE equalizers which adaptively track the fast-fading channels. Commonly-used channel tracking schemes, Decision-Directed Recursive Least Square (DD/RLS), Per-Survivor Processing Recursive Least Square (PSP/RLS) and other reduced-complexity MLSE alg...