System Identification Using Adaptive Algorithms

In this paper, System Identification is accomplished using various adaptive filters. System Identification is the one which is used in identifying the unknown model of a system and it is the mathematical modeling of the plant or process. It is said to be the bridge between the real-time application and the mathematical model of a particular system with respect to control theory and abstraction of model. In order to identify the unknown system, LMS (Least Mean Square), RLS (Recursive Least Square), NLMS (Normalized Least Mean Square), Leaky LMS, and Block LMS algorithms are used. Based on the mean square error obtained from the above-mentioned algorithms for the unknown system, the comparison of simulation results is done for the performance analysis of the adaptive algorithms.