6533b7d9fe1ef96bd126cc20
RESEARCH PRODUCT
Reduced complexity models in the identification of dynamical networks: Links with sparsification problems
Laura GiarreGiacomo InnocentiDonatello Materassisubject
Approximation theoryMathematical optimizationSettore ING-INF/04 - AutomaticaDynamical systems theoryComputational complexity theoryNode (networking)A priori and a posteriorisparsification compressing sensing estimation networksNetwork topologyGreedy algorithmTopology (chemistry)Mathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-12-01 | Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference |