The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input auto-correlation matrix is ill-conditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the ad...

Abstract. Adaptive inverse control of linear system with fixed learning rate least mean square (LMS) algorithm is improved by varying the learning rate. This variable learning rate LMS algorithm is proved to be convergent by using Lyapunov method. It has better performance especially when there is noise in command input signal. And it is simpler than the Variable Step-size Normalized LMS algori...

AbdvactThe effects of quantization in an LMS-Newton adaptive filtering algorithm are investigated. The algorithm considered uses an optimum convergence factor, that forces the output ayoclteriori error to become zero in each iteration. The prOpagation of errors due to quantization in the internal variables of the algorithm is investigated and a closed-form formula for the excess mean square err...

Designing a Least Mean Square (LMS) family adaptive algorithm includes solving the wellknown trade-off between the initial convergence speed and the mean-square error in steady state according to the requirements of the application at hands. The trade-off is controlled by the step-size parameter of the algorithm. Large step size leads to a fast initial conver‐ gence but the algorithm also exhib...

The least mean-square (LMS) filter is one of the most common adaptive linear estimation algorithms. In many practical scenarios, and particularly in digital communications systems, the signal of interest (SOI) and the input signal are jointly wide-sense cyclostationary. Previous works analyzing the performance of LMS filters for this important case assume specific probability distributions of t...

Adaptive filters updated by the least mean square (LMS) algorithm are succesfully implemented in digital control systems. They are utilized for both plant identification and control purposes. In control appications (e.g. in active noise control) the output of the adaptive filter drives the plant input, and the error signal is derived only at the output of the plant. In such cases the filtered r...

The two-dimensional least mean square ( 2 0 LMS) adaptive filters have been recently used in the image processing applications for reducing the noise. I n this paper, a new two-dimensional LMS algorithm is proposed. For the special desires to the linear phase constraint during filtering the images, a n additional linear phase constraint is added to the existing 2 0 LMS algorithms. Compare with ...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

Adaptive filters are used in a number of applications, many of which can benefit from a reduction in power. In this paper we present derivations of the approximate expressions used in [1] for the increase in mean square error of the LMS adaptive algorithm when the total processing power is decreased.

A standard algorithm for LMS-filter simulation, tested with several convergence criteria is presented in this paper. We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when random functions are used as reference inputs. In this paper, we make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix...