identification of time-varying systems with fast changing parameters using forgetting factor approach
In this paper, the basic principle of parameter identification of TVARX model using Recursive method with Forgetting Factor is given. Physical phenomena exhibit nonstationary or time varying behavior for a number of reasons. System identification is an experimental approach for determining the dynamic model of a system. One of the key elements for implementing this system model identification approach is the parameter adaptation algorithm, which drives the parameters of the adjustable prediction model from the data available at each sampling instant. The input is chosen as pseudo-random binary sequence which is frequency rich signal. The results are taken for fast varying parameters of first order TVARX model and the effect of variable forgetting factor is also observed. The performance is evaluated by calculating different performance measures of true and estimated values of model parameters for noisy and without noisy conditions. From the results, estimated parameters are nearly equal to that of actual model and small error is found at the instant of parameters changing.
tvarx model recursive method parameter estimation time varying systems