Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6952546 | Journal of the Franklin Institute | 2018 | 16 Pages |
Abstract
In this paper, we consider the parameter estimation issues of a class of multivariate output-error systems. A decomposition based recursive least squares identification method is proposed using the hierarchical identification principle and the auxiliary model idea, and its convergence is analyzed through the stochastic process theory. Compared with the existing results on parameter estimation of multivariate output-error systems, a distinct feature for the proposed algorithm is that such a system is decomposed into several sub-systems with smaller dimensions so that parameters to be identified can be estimated interactively. The analysis shows that the estimation errors converge to zero in mean square under certain conditions. Finally, in order to show the effectiveness of the proposed approach, some numerical simulations are provided.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Yanjiao Wang, Feng Ding, Minhu Wu,