Variable Step Size of LMS Algorithem Using Partical Swarm Optimization

Abstract

In this paper a novel method using both Particle Swarm Optimization (PSO) and least mean Square algorithm (LMS) is proposed. The main parameters tap-length and tap-weight are updated using the PSO and the LMS algorithm respectively according to the value of mean square error (MSE).By utilizing such an approach, both a fast convergence rate and a small steady-state MSE can be obtained. Although many LMS algorithmic methods perform well under certain conditions, performance can be degrade by noise and having performance sensitivity over parameter setting. In this paper, a new concept is introduced to vary the step size based upon evolutionary programming (SSLMSEV) algorithm is described. It has shown that the performance generated by this method is robust and does not require any pre-setting of involved parameters in solution based upon statistical characteristics of signal.