For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during station...

New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. T ime constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the ste...

An improved Infinite Impulse Response (IIR) Least Mean Squares (LMS) algorithm using parallel filters and evolutionary programming techniques is introduced. IIR filters have the attractive property that they require fewer computations than a corresponding FIR filter, but they are prone to instability and local minimum problems. Evolutionary algorithms are good in global optimization scenarios, ...

This paper proposes an improved adaptive harmonic IIR notch filter. The proposed algorithm utilizes varying notch bandwidth and convergence factor to achieve robust frequency estimation and tracking. A formula to determine the stability bound by using the LMS (least mean squares) algorithm is derived. In addition, the developed algorithm is also devised to prevent the adaptive algorithm from co...

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addi...

The problem of constructing adaptive minimum bit error rate (MBER) linear multiuser detectors is considered for direct-sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style stochastic gradient adaptive algor...

In this correspondence, a least mean squares (LMS)-based algorithm is devised for unbiased system identification in the presence of white input and output noise, assuming that the ratio of the noise powers is known. The proposed approach aims to minimize the mean square value of the equation-error function under a constant-norm constraint and is equivalent to minimizing a modified mean square e...

In the last ten years, there has been much research on active noise control (ANC) systems and transaural sound reproduction (TSR) systems. In those fields, multichannel FIR adaptive filters are extensively used. For the learning of FIR adaptive filters, recursive-least-squares (RLS) algorithms are known to produce a faster convergence speed than stochastic gradient descent techniques, such as t...

Adaptive beamforming has been studied extensively from a simulation point of view. While existing works compare various techniques based on their output performance, emulation hardware systems and the prerequisite analysis firmware viability remain relatively unexplored. The work presented in this paper addresses two issues. One is implementation adaptive Hardware Description Language Least Mea...