2 research outputs found
Prediction error identification of linear dynamic networks with rank-reduced noise
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 201
Abstractions of linear dynamic networks for input selection in local module identification
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.Comment: 17 pages, 7 figures. Paper to appear in Automatica, Vol. 117, July
202