Prediction error identification of linear dynamic networks with rank-reduced noise
| Autor: | Weerts, Harm H. M., Hof, Paul M. J. Van den, Dankers, Arne G. |
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| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Automatica, Vol. 98, pp. 256-268, December 2018 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.automatica.2018.09.033 |
| Popis: | Dynamic networks are interconnected dynamic systems with measured node signals and dynamic modules reflecting the links between the nodes. We address the problem of \red{identifying a dynamic network with known topology, on the basis of measured signals}, for the situation of additive process noise on the node signals that is spatially correlated and that is allowed to have a spectral density that is singular. A prediction error approach is followed in which all node signals in the network are jointly predicted. The resulting joint-direct identification method, generalizes the classical direct method for closed-loop identification to handle situations of mutually correlated noise on inputs and outputs. When applied to general dynamic networks with rank-reduced noise, it appears that the natural identification criterion becomes a weighted LS criterion that is subject to a constraint. This constrained criterion is shown to lead to maximum likelihood estimates of the dynamic network and therefore to minimum variance properties, reaching the Cramer-Rao lower bound in the case of Gaussian noise. Comment: 17 pages, 5 figures, revision submitted for publication in Automatica, 4 April 2018 |
| Databáze: | arXiv |
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