Please use this identifier to cite or link to this item:
        
        
        
        http://acervodigital.unesp.br/handle/11449/67300Full metadata record
| DC Field | Value | Language | 
|---|---|---|
| dc.contributor.author | Francisco, Gerson | - | 
| dc.contributor.author | Muruganandam, Paulsamy | - | 
| dc.date.accessioned | 2014-05-27T11:20:40Z | - | 
| dc.date.accessioned | 2016-10-25T18:18:39Z | - | 
| dc.date.available | 2014-05-27T11:20:40Z | - | 
| dc.date.available | 2016-10-25T18:18:39Z | - | 
| dc.date.issued | 2003-06-01 | - | 
| dc.identifier | http://dx.doi.org/10.1103/PhysRevE.67.066204 | - | 
| dc.identifier.citation | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, v. 67, n. 6 2, 2003. | - | 
| dc.identifier.issn | 1063-651X | - | 
| dc.identifier.uri | http://hdl.handle.net/11449/67300 | - | 
| dc.identifier.uri | http://acervodigital.unesp.br/handle/11449/67300 | - | 
| dc.description.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. | en | 
| dc.language.iso | eng | - | 
| dc.source | Scopus | - | 
| dc.subject | Algorithms | - | 
| dc.subject | Boundary conditions | - | 
| dc.subject | Eigenvalues and eigenfunctions | - | 
| dc.subject | Forecasting | - | 
| dc.subject | Matrix algebra | - | 
| dc.subject | Probability | - | 
| dc.subject | Probability distributions | - | 
| dc.subject | Random processes | - | 
| dc.subject | Statistical methods | - | 
| dc.subject | Vectors | - | 
| dc.subject | Bayesian modeling | - | 
| dc.subject | Dynamical systems theory | - | 
| dc.subject | Finite time prediction | - | 
| dc.subject | Local dimension | - | 
| dc.subject | Spatiotemporal chaotic system | - | 
| dc.subject | Chaos theory | - | 
| dc.title | Local dimension and finite time prediction in spatiotemporal chaotic systems | en | 
| dc.type | outro | - | 
| dc.contributor.institution | Universidade Estadual Paulista (UNESP) | - | 
| dc.contributor.institution | Bharathidasan University | - | 
| dc.description.affiliation | Instituto de Fisica Teorica Universidade Estadual Paulista, 01405-900 Sao Paulo, SP | - | 
| dc.description.affiliation | Center for Nonlinear Dynamics Department of Physics Bharathidasan University, Tiruchirapalli 620024, Tamil Nadu | - | 
| dc.description.affiliationUnesp | Instituto de Fisica Teorica Universidade Estadual Paulista, 01405-900 Sao Paulo, SP | - | 
| dc.identifier.doi | 10.1103/PhysRevE.67.066204 | - | 
| dc.identifier.wos | WOS:000184085000038 | - | 
| dc.rights.accessRights | Acesso aberto | - | 
| dc.identifier.file | 2-s2.0-42749108043.pdf | - | 
| dc.relation.ispartof | Physical Review E: Statistical, Nonlinear, and Soft Matter Physics | - | 
| dc.identifier.scopus | 2-s2.0-42749108043 | - | 
| Appears in Collections: | Artigos, TCCs, Teses e Dissertações da Unesp | |
There are no files associated with this item.
    
 
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
