Linear system identification by autoregressive model decomposition

Abstract

Fast, simple and parallel identification algorithms are highly desirable in many applications. However, the assumptions which make the algorithm fast and simple also make the algorithm sensitive to deviations from these assumptions. In view of these considerations, this paper develops a sequential algorithm for the identification of discrete-time linear systems. The identification algorithm is based on decomposition of the autoregressive model. This decomposition approach identifies autoregressive model coefficients by minimizing the squared error performance index by use of the multilevel hierarchical decomposition procedure and by use of the stochastic approximation algorithm. Computer simulations illustrate the performance of the identification algorithm and comparison of the results with those obtained from the undecomposed stochastic approximation algorithm.