System Identification is one of the most interesting applications for adaptive filters, especially for the Least Mean Square algorithm, due to its robustness and calculus simplicity. Based on the error signal, the filter’s coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. The application enables remarkable developments and research, creating an opportunity for automation and prediction. In this paper we focus on parameters of system identification by changing design parameters such as forgetting factor, filter length, initial value of filter weight and input variance of filter through MATLAB/SIMULINK Software

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RLS Adaptive Filter Forgetting Factor Filter length Filter weight Input Variance