In many applicative scenarios it is important to derive information about the topology and the internal connections of more dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology. We cast the problem as the optimization of a cost function operating a trade-off between accuracy and complexity in the final model. We address the problem of reducing the complexity by fixing a certain degree of sparsity, and trying to find the solution that ``better'' satisfies the constraints according to the criterion of approximation.

Materassi, D., Innocenti, G., Giarre L (2009). Reduced Complexity Models in the Identifi cation of Dynamical Networks: links with sparsi cation problems.. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? IEEE Conference on Decisions and Control, Shanghai.

Reduced Complexity Models in the Identifi cation of Dynamical Networks: links with sparsi cation problems.

GIARRE, Laura
2009-01-01

Abstract

In many applicative scenarios it is important to derive information about the topology and the internal connections of more dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology. We cast the problem as the optimization of a cost function operating a trade-off between accuracy and complexity in the final model. We address the problem of reducing the complexity by fixing a certain degree of sparsity, and trying to find the solution that ``better'' satisfies the constraints according to the criterion of approximation.
dic-2009
IEEE Conference on Decisions and Control
Shanghai
December 2010
2009
6
Materassi, D., Innocenti, G., Giarre L (2009). Reduced Complexity Models in the Identifi cation of Dynamical Networks: links with sparsi cation problems.. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? IEEE Conference on Decisions and Control, Shanghai.
Proceedings (atti dei congressi)
Materassi, D; Innocenti, G; Giarre L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/59845
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