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 …

In order to identify one system (module) in an interconnected dynamic network, one typically
has to solve a Multi-Input-Single-Output (MISO) identification problem that requires …

In classical approaches of dynamic network identification, in order to identify a system
(module) embedded in a dynamic network, one has to formulate a Multi-input-Single-output …

In abstractions of linear dynamic networks, selected node signals are removed from the
network, while kee** the remaining node signals invariant. The topology and link …

This work focuses on the identifiability of dynamical networks with partial excitation and
measurement: a set of nodes are interconnected by unknown transfer functions according to …

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 …

This article addresses the problem of consistently identifying a single transfer function in a
network of dynamic systems using only observational data. It is assumed that the topology is …

For the problem of identifying a target module that is embedded in a dynamic network with
known interconnection structure, different sets of conditions are available for the set of node …

This work focuses on the generic identifiability of dynamical networks with partial excitation
and measurement: a set of nodes are interconnected by transfer functions according to a …

System identification problems utilizing a prediction error approach are typically considered
in an input/output setting, where a directional cause-effect relationship is presumed and …