In the literature methods have been proposed which enable consistent estimates of modules embedded in complex dynamic networks. In this paper the network extension of the so called closed-loop Direct Method is investigated. Currently, for this method the variables which must be included in the predictor model are not considered as a user choice. In this paper it is shown that there is some freedom as to which variables to include in the predictor model as inputs, and still obtain consistent estimates of the module of interest. Conditions on this choice of predictor inputs are presented.
|Title of host publication||Proceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze|
|Place of Publication||Firenze, Italy|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2013|
|Event||52nd IEEE Conference on Decision and Control (CDC 2013) - Florence, Italy|
Duration: 10 Dec 2013 → 13 Dec 2013
Conference number: 52
|Conference||52nd IEEE Conference on Decision and Control (CDC 2013)|
|Abbreviated title||CDC 2013|
|Period||10/12/13 → 13/12/13|
Dankers, A. G., Hof, Van den, P. M. J., & Heuberger, P. S. C. (2013). Predictor input selection for direct identification in dynamic networks. In Proceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze Institute of Electrical and Electronics Engineers.