Local dimension and finite time prediction in spatiotemporal chaotic systems


Autoria(s): Francisco, Gerson; Muruganandam, Paulsamy
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

27/05/2014

27/05/2014

01/06/2003

Resumo

Predictability 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.

Identificador

http://dx.doi.org/10.1103/PhysRevE.67.066204

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, v. 67, n. 6 2, 2003.

1063-651X

http://hdl.handle.net/11449/67300

10.1103/PhysRevE.67.066204

WOS:000184085000038

2-s2.0-42749108043

2-s2.0-42749108043.pdf

Idioma(s)

eng

Relação

Physical Review E: Statistical, Nonlinear, and Soft Matter Physics

Direitos

openAccess

Palavras-Chave #Algorithms #Boundary conditions #Eigenvalues and eigenfunctions #Forecasting #Matrix algebra #Probability #Probability distributions #Random processes #Statistical methods #Vectors #Bayesian modeling #Dynamical systems theory #Finite time prediction #Local dimension #Spatiotemporal chaotic system #Chaos theory
Tipo

info:eu-repo/semantics/article