Momentum Stochastic Gradient Algorithm With Data Filtering for Generalized Time‐Varying Systems

ABSTRACT This article proposes an accelerated gradient‐based parameter estimation for generalized time‐varying systems. A momentum term is incorporated into the parameter adaptation law to accelerate convergence and suppress oscillatory behavior. In addition, the data filtering technique is applied to preprocess the input and output signals, effectively attenuating the influence of colored noise on parameter estimation. The proposed algorithm is further validated through its application to a DC motor system.