Local dimension and finite time prediction in spatiotemporal chaotic systems

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorBharathidasan University
dc.date.accessioned2014-05-27T11:20:40Z
dc.date.available2014-05-27T11:20:40Z
dc.date.created2014-05-27T11:20:40Z
dc.date.issued2003-06-01
dc.identifierPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics, v. 67, n. 6 2, 2003.
dc.identifier1063-651X
dc.identifierhttp://hdl.handle.net/11449/67300
dc.identifier10.1103/PhysRevE.67.066204
dc.identifierWOS:000184085000038
dc.identifier2-s2.0-42749108043
dc.identifier2-s2.0-42749108043.pdf
dc.description.abstractPredictability is related to the uncertainty in the outcome of future events during the evolution of the state of a system. The cluster weighted modeling (CWM) is interpreted as a tool to detect such an uncertainty and used it in spatially distributed systems. As such, the simple prediction algorithm in conjunction with the CWM forms a powerful set of methods to relate predictability and dimension.
dc.languageeng
dc.relationPhysical Review E: Statistical, Nonlinear, and Soft Matter Physics
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectAlgorithms
dc.subjectBoundary conditions
dc.subjectEigenvalues and eigenfunctions
dc.subjectForecasting
dc.subjectMatrix algebra
dc.subjectProbability
dc.subjectProbability distributions
dc.subjectRandom processes
dc.subjectStatistical methods
dc.subjectVectors
dc.subjectBayesian modeling
dc.subjectDynamical systems theory
dc.subjectFinite time prediction
dc.subjectLocal dimension
dc.subjectSpatiotemporal chaotic system
dc.subjectChaos theory
dc.titleLocal dimension and finite time prediction in spatiotemporal chaotic systems
dc.typeArtículos de revistas


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