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...

In this paper, we study the mean square convergence of the kernel least mean square (KLMS). The fundamental energy conservation relation has been established in feature space. Starting from the energy conservation relation, we carry out the mean square convergence analysis and obtain several important theoretical results, including an upper bound on step size that guarantees the mean square con...

The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Several variable step-size strategies have been suggested to improve the performance of the LMS algorithm. These strategies enhance the performance of the algorithm but a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find ...

Elimination of tainted noise and improving the overall quality of a speech signal is speech enhancement. To gain the advantage of individual algorithms we propose a new linear model and that is in the form of cascade adaptive filters for suppression of non-stationary noise. We have successfully deployed NLMS (Normalized Least Mean Square) algorithm, Sign LMS (Least Mean Square) and RLS (Recursi...

A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...

In this technical report we analyse the performance of diffusion strategies applied to the Least-Mean-Square adaptive filter. We configure a network of cooperative agents running adaptive filters and discuss their behaviour when compared with a non-cooperative agent which represents the average of the network. The analysis provides conditions under which diversity in the filter parameters is be...

The present system of speech enhancement is developing by adaptive filtering approach in digital filters. The adaptive filter utilizes the least mean square algorithm for noise removal, but in practical application of LMS algorithm, a key parameter is the step size. As it is known, if the step size is large, the convergence rate of LMS algorithm will be rapid, but the steady-state mean square e...

In this paper, a technique to determine the filter bank coefficients of Wavelets db3 and coif2 using mean square error adaptive filter RLS algorithm has been presented. Filter bank coefficients of the wavelet are treated as weight vector of adaptive filter and are changed iteratively to reach the desired value after few iteration. It is found that of the three adaptive algorithms viz. Least Mea...