Least squares based iterative algorithms for identifying Box–Jenkins models with finite measurement data

A least squares based iterative identification algorithm is developed for Box–Jenkins models (or systems). The proposed iterative algorithm can produce highly accurate parameter estimation compared with recursive approaches. The basic idea of the proposed iterative method is to adopt the interactive estimation theory: the parameter estimates relying on unknown variables are computed by using the estimates of these unknown variables which are obtained from the preceding parameter estimates. The numerical example indicates that the proposed iterative algorithm has fast convergence rates compared with the gradient based iterative algorithm.