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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/67300
Title: 
Local dimension and finite time prediction in spatiotemporal chaotic systems
Author(s): 
Institution: 
  • Universidade Estadual Paulista (UNESP)
  • Bharathidasan University
ISSN: 
1063-651X
Abstract: 
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.
Issue Date: 
1-Jun-2003
Citation: 
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, v. 67, n. 6 2, 2003.
Keywords: 
  • 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
Source: 
http://dx.doi.org/10.1103/PhysRevE.67.066204
URI: 
Access Rights: 
Acesso aberto
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/67300
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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