Aldian Ambark Shashoa, NasarS. Elmezughi, AbdurrezagN. Jleta, IbrahimB. Ekreem, Nasser2021-06-302021-06-302019-12-23Recent Innovations in Mechatronics, Vol. 6 No. 1 (2019) , 1-6.https://hdl.handle.net/2437/319793In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.application/pdfsystem identificationmodel validationparameter estimationhistogramBox–Jenkins systemsystem identificationmodel validationparameter estimationhistogramBox–Jenkins systemfolyóiratcikkOpen Accessby the authorshttps://doi.org/10.17667/riim.2019.1/4.Recent Innovations in Mechatronics162064-9622