Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10677609 | Applied Mathematical Modelling | 2016 | 13 Pages |
Abstract
In this paper, we use a noise transfer function to filter the input-output data and propose a new recursive algorithm for multiple-input single-output systems under the maximum likelihood principle. The main contributions of this paper are to derive a filtering based maximum likelihood recursive least squares (F-ML-RLS) algorithm for reducing computational burden and to present two recursive least squares algorithms to show the effectiveness of the F-ML-RLS algorithm. In the end, an illustrative simulation example is provided to test the proposed algorithms and we show that the F-ML-RLS algorithm has a high computational efficiency with smaller sizes of its covariance matrices and can produce more accurate parameter estimates.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Feiyan Chen, Feng Ding,