文章基本信息

标题：Identification and Prediction in Dynamic Networks with Unobservable Nodes
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作者：Jonas Linder ; Martin Enqvist
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：10574-10579
DOI：10.1016/j.ifacol.2017.08.1306
语种：English
出版社：Elsevier
摘要：AbstractThe 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.
关键词：KeywordsDynamic networksclosed-loop identificationidentifiabilitysystem identification