Local module identification in dynamic networks with correlated noise: The full input case

Paul M.J. Van Den Hof, Karthik R. Ramaswamy, Arne G. Dankers, Giulio Bottegal

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
7 Downloads (Pure)


The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, typically under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is an algorithm that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. As a first step in the analysis, we consider the situation where the selected output node signals are predicted based on all of their in-neighbor node signals in the network.

Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728113982
Publication statusPublished - Dec 2019
Event58th IEEE Conference on Decision and Control (CDC 2019) - Nice, France
Duration: 11 Dec 201913 Dec 2019


Conference58th IEEE Conference on Decision and Control (CDC 2019)
Abbreviated titleCDC 2019
Internet address


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