This paper presents a modified version of the twostep

least-mean-square (LMS)-type adaptive algorithm motivated

by the work of Gazor. We describe the nonstationary adaptation

characteristics of this modified two-step LMS (MG-LMS) algorithm

for the system identification problem. It ensures stable behavior

during convergence as well as improved tracking performance

in the smoothly time-varying environments.