Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

This study investigates the ability of recursive least squares (RLS) and mean square (LMS) adaptive filtering algorithms to predict quickly track unknown systems. Tracking system behavior is important if there are other parallel systems that must follow exactly same at time. The algorithm can correct filter coefficients according changes in parameters minimize errors between output for input signal. RLS LMS were designed then examined separately, giving them a similar signal was given system. difference showed performance each when identifying an two filters able system, but certain advantages over other. had advantage faster convergence fewer steady-state than algorithm, less computational complexity.