Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression Control

Convergence Analysis of Proportionate-type Least Mean Square Algorithms

In this paper, we present the convergence analysis of proportionate-type least mean square (Pt-LMS) algorithm that identifies the sparse system effectively and more suitable for real time VLSI applications. Both first and second order convergence analysis of Pt-LMS algorithm is studied. Optimum convergence behavior of Pt-LMS algorithm is studied from the second order convergence analysis provid...

A Modified Least Mean Square Method Applied to Frequency Relaying

Mean square convergence analysis for kernel least mean square algorithm

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...

Normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output channels

This paper studies normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output (MIMO) channels. Although the MIMO channel is often modeled as sparse, traditional normalized least mean square-based filtering algorithm never takes the advantage of the inherent sparse structure information and thus causes some performance loss. Unlike the t...

Least Mean Square Algorithm

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...