LMS algorithm is simple and is well suited for continuous transmission systems since it is a continuously adaptive algorithm. However, it is not known for its convergence speed in the presence of Gaussian, spatially white, of null mean and variance which has prompted people to use other complicated algorithms. In the above scenario LMS has maximum mean square error and minimum error stability. ...

For the simple layer potential V that is associated with the 3D Laplacian, we consider the weakly singular integral equation V φ = f . This equation is discretized by the lowest order Galerkin boundary element method. We prove convergence of an h-adaptive algorithm that is driven by a weighted residual error estimator. Moreover, we identify the approximation class for which the adaptive algorit...

Elastic boundary eigensolution problems are converted into boundary integral equations by potential theory. The kernels of the boundary integral equations have both the logarithmic and Hilbert singularity simultaneously. We present the mechanical quadrature methods for solving eigensolutions of the boundary integral equations by dealing with two kinds of singularities at the same time. The meth...

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...

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...

This paper introduces a novel constraint adaptive filtering algorithm based on a relative logarithmic cost function which is termed as Constrained Least Mean Logarithmic Square (CLMLS). The proposed CLMLS algorithm elegantly adjusts the cost function based on the amount of error thereby achieves better performance compared to the conventional Constrained LMS (CLMS) algorithm. With no assumption...

Combined with strong tracking filter (STF) theory, the Strong Tracking Square-Root Unscented Kalman Filter (UKF)-based satellite attitude determination algorithm is proposed in this paper. QR decomposition and Cholesyk decomposition are introduced in this paper, which improves the stability of filter. In addition, by introduced adaptive fading factor, the prediction error covariance matrix can ...

This paper investigates the tracking characteristics of Frequency domain least-mean-square (FDLMS) adaptive filters and Time domain least-mean square (TDLMS) adaptive filters and compares the convergence performance of TDLMS and FDLMS adaptive algorithms for both real and complex valued signals. We simulated the adaptive filter using MATLAB, and the results validate the better performance of FD...

We consider the adaptive lowest-order boundary element method (ABEM) based on isotropic mesh-refinement for the weakly-singular integral equation for the 3D Laplacian. The proposed scheme resolves both, possible singularities of the solution as well as of the given data. The implementation thus only deals with discrete integral operators, i.e. matrices. We prove that the usual adaptive mesh-ref...

The aim of the Speech Enhancement system is to improve the quality of noisy speech signal. This paper emphasize on enhancement of noisy speech by using Affine Projection Algorithm (APA) and Kernel Affine Projection Algorithm (KAPA). Noise is present everywhere in the environment, So Kernel adaptive filters are used to enhance noisy speech signal and shows the good improvement in increasing the ...