This paper develops a novel passive stochastic gradient algorithm. In approximation, the algorithm does not have control over location where noisy gradients of cost function are evaluated. Classical algorithms use kernel that approximates Dirac delta to weigh based on how far they evaluated from desired point. this we construct multi-kernel The performs substantially better in high dimensional ...

In this paper a novel constrained-stability least-mean-squares (LMS) algorithm for filtering speech sounds is proposed in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the weight vector change under a stability constraint over the a posteriori estimation errors. To this purpose, the Lagrangian methodology has been used in order t...

A novel approach for the least-mean-square (LMS) estimation algorithm is proposed. Rather than using a fixed convergence parameter μ, this approach utilizes a time-varying LMS parameter μn. This technique leads to faster convergence and provides reduced mean-squared error compared to the conventional fixed parameter LMS algorithm. The algorithm has been tested for noise reduction and estimation...

Multi-rate digital signal processing techniques have been developed in recent years for a wide range of applications, such as speech and image compression, statistical and adaptive signal processing and digital audio. Multi-rate statistical and adaptive signal processing methods provide solution to original signal reconstruction, using observation signals sampled at different rates. In this stu...

A nonlinear regression model with correlated, normally distributed errors is investigated. The bias and the mean square error matrix of the approximate least squares estimator of regression parameters are derived and their limit properties are studied.

This paper represents a comparative Study of filter algorithms Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) by considering a Quasi Orthogonal Space Time Block Code (QOSTBC) encoded Multiple Input Multiple output (MIMO) Code Division Multiple Access (CDMA) system. MIMO-CDMA system has been currently acknowledged as one of the most competitive tech...

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...

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...

An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical...