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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. ©2009 IEEE.

Reduced complexity models in the identification of dynamical networks: Links with sparsification problems / D., Materassi; G., Innocenti; Giarrè, Laura. - (2009), pp. 4796-4801. (Intervento presentato al convegno 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 tenutosi a Shanghai, chn nel 2009) [10.1109/CDC.2009.5400379].

Reduced complexity models in the identification of dynamical networks: Links with sparsification problems

GIARRÈ, Laura
2009

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. ©2009 IEEE.
2009
48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Shanghai, chn
2009
4796
4801
D., Materassi; G., Innocenti; Giarrè, Laura
Reduced complexity models in the identification of dynamical networks: Links with sparsification problems / D., Materassi; G., Innocenti; Giarrè, Laura. - (2009), pp. 4796-4801. (Intervento presentato al convegno 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 tenutosi a Shanghai, chn nel 2009) [10.1109/CDC.2009.5400379].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1123572
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