Identification and Prediction in Dynamic Networks with Unobservable Nodes

The interest for system identification in dynamic networks has increased recently with a wide variety of applications. In many cases, it is intractable or undesirable to observe all nodes in a network and thus, to estimate the complete dynamics. Furthermore, it might even be challenging to estimate...

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Bibliographic Details
Main Authors: Linder, Jonas, Enqvist, Martin
Format: Conference Proceeding
Language:English
Subjects:
closed-loop identification
Dynamic networks
identifiability
system identification
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Summary:The interest for system identification in dynamic networks has increased recently with a wide variety of applications. In many cases, it is intractable or undesirable to observe all nodes in a network and thus, to estimate the complete dynamics. Furthermore, it might even be challenging to estimate a subset of the network if key nodes are unobservable due to correlation between the nodes. In this contribution, we will discuss an approach to treat this problem. The approach relies on additional measurements that are dependent on the unobservable nodes and thus indirectly contain information about them. These measurements are used to form an alternative indirect model that is only dependent on observed nodes. The purpose of estimating this indirect model can be either to recover information about modules in the original network or to make accurate predictions of variables in the network. Examples are provided for both recovery of the original modules and prediction of nodes.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2017.08.1306