On the well-posedness of LTI networks

Woodbury, Nathan; Dankers, Arne; Warnick, Sean

doi:10.1109/cdc.2017.8264370

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…• For the two-stage identification method, consistency of the estimate Ĝji is achievable if there is a sufficient excitation by external excitation signals in the network; • For the direct identification method, consistency of the estimate Ĝji is achievable, if besides sufficient excitation by external excitation and disturbance signals, correlated noises between inputs and outputs are taken care of. This can be done by either choosing the node sets S, L, V, Z such that these correlated noises (or confounding variables) do not occur, Dankers et al (2017), or by modelling this noise correlation correctly in the model, leading to the so-called joint-direct method (Weerts et al, 2018c;Van den Hof et al, 2019).…”

Section: Identification Methodsmentioning

confidence: 99%

“…To this end the concept of network abstraction is defined next. This definition is related to the notions of abstraction in (Pappas and Sastry, 1996;Woodbury et al, 2017).…”

Section: Abstractionmentioning

confidence: 99%

“…A systematic way of selecting the measured node signals is provided. The selection method is extended in (Dankers et al, 2017) to handle situations of confounding variables being caused by disturbances that are spatially correlated over the different node signals. This is particularly of interest when applying the direct method, aiming at maximum likelihood results.…”

Section: Introductionmentioning

confidence: 99%

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Abstractions of linear dynamic networks for input selection in local module identification

et al. 2020

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In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics, or modules, of an abstracted network will generally be changed compared to the original network. Abstractions of dynamic networks can be used to select an appropriate set of node signals that are to be measured, on the basis of which a particular local module can be estimated. A method is introduced for network abstraction that generalizes previously introduced algorithms, as e.g. immersion and the method of indirect inputs. For this abstraction method it is shown under which conditions on the selected signals a particular module will remain invariant. This leads to sets of conditions on selected measured node variables that allow identification of the target module.

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Section: Identification Methodsmentioning

confidence: 99%

“…To this end the concept of network abstraction is defined next. This definition is related to the notions of abstraction in (Pappas and Sastry, 1996;Woodbury et al, 2017).…”

Section: Abstractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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