Comparison of based adaptive predictive schemes for improvement of tracking randomly time-varying systems

This paper presents a comparison between three based adaptive predictive schemes used in order to improve the tracking capability of the LMS algorithm. We identify system variations modeled by a random walk. Using a theoretical analysis and simulation results, we illustrate the contribution of coupled adaptive prediction and system identification for highly correlated stationary inputs and nonstationary (speech) inputs.