Abstract
For consistent or minimum variance estimation of a single module in a dynamic network, a predictor model has to be chosen with selected inputs and outputs, composed of a selection of measured node signals and possibly external excitation signals. The predictor model has to be chosen in such a way that consistent estimation of the target module is possible, under the condition that we have data-informativity for the considered predictor model set. Consistent and minimum variance estimation of target modules is typically obtained if we follow a direct method of identification and predictor model selection, characterized by the property that measured node signals are the prime predictor input signals. In this paper the concept of data-informativity for network models will be formalized, and for the direct method the required data-informativity conditions will be specified in terms of path-based conditions on the graph of the network model, guaranteeing data-informativity in a generic sense, i.e. independent on numerical values of the network transfer functions concerned.
Original language | English |
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Title of host publication | 2020 59th IEEE Conference on Decision and Control, CDC 2020 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 4354-4359 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-7447-1 |
DOIs | |
Publication status | Published - 14 Dec 2020 |
Event | 59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 |
Conference
Conference | 59th IEEE Conference on Decision and Control, CDC 2020 |
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Country | Korea, Republic of |
City | Virtual, Jeju Island |
Period | 14/12/20 → 18/12/20 |