The Recursive Identification of Stochastic Linear Dynamical Systems Simulation Study

This Paper deals with the recursive identification problem of stochastic linear dynamical systems , Important Algorithms are explained in the identification system domains that are time-varying, and using a recursive Least Square method with a famous approach to estimate the model parameter, that a forgetting factor, and a Kalman filter approach with different values for a best linear dynamic models, that are identified from the two type of stochastic linear dynamic systems: that the equation error models which contain ARX models and ARMAX models. Output error models which consist of OE and Box-Jenkins models, that are reached by using the suggested instrument in Off-Line Identification, where the exact Linear models reached their parameter stable with Time, Moreover, the Statistic terms are verified from a point of random errors and insignificant cross-correlation between inputs and outputs residual.