Search results for " Sparsification"

showing 2 items of 2 documents

Model Identification of a Network as Compressing Sensing

2013

In many applications, it is important to derive information about the topology and the internal connections of 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, and unveiling an unknown structure as the estimate of a "sparse Wiener filter". A geometric interpretation of the problem in a pre-Hilbert space for wide-sense stochastic processes is provided. We cast the problem as the optimization of a cost function where a set of parameters are used t…

IdentificationReduced modelTheoretical computer scienceGeneral Computer ScienceDynamical systems theoryComputer scienceNetworkTopology (electrical circuits)Dynamical Systems (math.DS)Systems and Control (eess.SY)Set (abstract data type)symbols.namesakeFOS: MathematicsFOS: Electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringMathematics - Dynamical SystemsMathematics - Optimization and ControlMathematics - General TopologySparsificationMechanical EngineeringWiener filterSystem identificationGeneral Topology (math.GN)Function (mathematics)Compressive sensingIdentification (information)Compressed sensingControl and Systems EngineeringOptimization and Control (math.OC)symbolsIdentification; Sparsification; Reduced models; Networks; Compressive sensingComputer Science - Systems and Control
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Reduced Complexity Models in the Identifi cation of Dynamical Networks: links with sparsi cation problems.

2009

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'' satis…

Settore ING-INF/04 - AutomaticaIdentification Sparsification Reduced Models Networks
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