LAUSR

This paper proposes an Aitken-based stochastic gradient algorithm for ARX models with time delay. By using the redundant rule, the ARX model can be transformed into an augmented model. Based on the Aitken method, the parameters of the augmented model can be estimated, and then, the unknown parameters of the ARX model and the time delay can be computed. The performance of the Aitken-based stochastic gradient algorithm is then analyzed. Furthermore, a numerical example and a real system example are provided to show the effectiveness of the proposed algorithm. Compared with the traditional stochastic gradient algorithm, the Aitken-based stochastic gradient algorithm achieves better convergence performance that the parameter estimation errors converge below 3% in both examples after 400 steps.