Comparative Analysis of various Adaptive Filtering Algorithms for Adaptive System Identification

Abstract

System identification is one of the most interesting applications for adaptive filters, for this dissertation provides a comparison of LMS, VSSLMS,NLMS and TDLMS adaptive algorithms. This process provided the best suitable algorithm for usage in adaptive filters for system identification. This technique Based on the error signal, where filter’s coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. Its applications include echo cancellation, channel equalization, interference cancellation, and so forth. Simulation results show that the proposed algorithms outperform the standard NLMS and TDLMS algorithms in both convergence rate and steady-state performance for sparse systems identification.