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CDC
2009
IEEE

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

8 years 9 months ago
Reduced complexity models in the identification of dynamical networks: Links with sparsification problems
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.
Donatello Materassi, Giacomo Innocenti, Laura Giar
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where CDC
Authors Donatello Materassi, Giacomo Innocenti, Laura Giarré
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