Teimour Tajdari;

【网址】：

http://casopisi.junis.ni.ac.rs/index.php/FUElectEnerg/article/view/6864

【刊名】：

Facta Universitatis. Series Electronics and Energetics

【ISSN】：

0353-3670

【出版信息】：

2021 Vol.34 No.1

【起止页】：

133 - 140,8

【关键词】：

RLS algorithmSteepest decent algorithmLMS algorithmSystem identification

【文摘】：

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

【code】：

机械工业信息研究院