We extend the use of the least squares method to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of a vector of a filter at iteration, we may compute the updated estimate of this vector at iteration upon the arrival of new data. In this paper, we propose a new tap-weight-updated RLS algorithm for an adaptive transversal fil...

This paper proposes an algorithm for detection in multiple input multiple output (MIMO) communication systems. The algorithm combines minimum mean square error (MMSE) and minimum mean square error with ordered successive interference cancellation (MMSE-OSIC) detectors with detection error estimation. Neyman-Pearson criterion is used and the decision boundary is derived. It helps the implementat...

The robust nonlinear adaptive filtering problem based on Volterra model is researched when the input and output observation data are both corrupted by noise in this paper. On the basis of minimizing Volterra total mean square error (VTMSE), a robust total least mean square adaptive filtering algorithm for nonlinear Volterra filter is proposed. The performance analysis demonstrates that the robu...

In this paper we provide a thorough ser( symbol error rate) analysis of two well known adaptive algorithms for equalization based on a novel least squares reference model that allows to treat the equalizer problem equivalently as system identification problem. An adaptive algorithm is a procedure for adjusting the parameters of an adaptive filter to minimize a cost function chosen for the task ...

Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS) algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE) crit...

In this paper, an artificial intelligence method particle swarm optimization (PSO) algorithm is presented for determining the optimal PI controller parameters for the indirect control active and reactive power of doubly fed induction generator (DFIG) to ensure a maximum power point tracking of a wind energy conversion system. A digital simulation is used in conjunction with the PSO algorithm to...

This study proposes a combination of a fuzzy sliding mode controller (FSMC) with integral-proportion-Derivative switching surface based superconducting magnetic energy storage (SMES) and PID tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. The goal of design is to improve the dynamic response of power systems after load demand changes. In t...

The goal of this paper is to present a novel variable length LMS (Least Mean Square) algorithm, in which the length of the adaptive filter is always a power of two and it is modified using an error estimate. Unlike former variable length stochastic gradient adaptive techniques, the proposed algorithm works in non-stationary situations. The implementation of the adaptive filter is described and ...

Adaptive methods for numerical solutions of differential equations are useful to combine the two goals of good accuracy and efficiency. This thesis contributes to the theoretical understanding of optimal convergence rates of adaptive algorithms. In particular, this work studies stopping, accuracy and efficiency behavior of adaptive algorithms for solving ordinary differential equations (ODEs), ...