Document Detail


Reconstructing bifurcation diagrams from noisy time series using nonlinear autoregressive models.
MedLine Citation:
PMID:  11969857     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
We introduce a formalism for the reconstruction of bifurcation diagrams from noisy time series. The method consists in finding a parametrized predictor function whose bifurcation structure is similar to that of the given system. The reconstruction algorithm is composed of two stages: model selection and bifurcation parameter identification. In the first stage, an appropriate model that best represents all the given time series is selected. A nonlinear autoregressive model with polynomial terms is employed in this study. The identification of the bifurcation parameters from among the many model parameters is done in the second stage. The algorithm works well even for a limited number of time series.
Authors:
E Bagarinao; K Pakdaman; T Nomura; S Sato
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics     Volume:  60     ISSN:  1063-651X     ISO Abbreviation:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics     Publication Date:  1999 Jul 
Date Detail:
Created Date:  2002-04-23     Completed Date:  2002-08-27     Revised Date:  2003-10-31    
Medline Journal Info:
Nlm Unique ID:  9887340     Medline TA:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1073-6     Citation Subset:  -    
Affiliation:
Division of Biophysical Engineering, Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University, Toyonaka City, Osaka 560-8531, Japan.
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