Presentation 2013-09-27
State Estimation for Large Scale Nonlinear Systems by using the Covariance Structure Analysis for Multivariate Time Series and Particle Filters
Shozo TOKINAGA, Yoshikazu IKEDA,
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Abstract(in English) This report deals with the state estimation for large scale nonlinear systems by using the covariance structure analysis for multivariate time series and the PF. At first, we propose an approximate representation of multivariate time series by using the representative time series called latent time series based on the covariance structure analysis where correlation among observed time series is utilized. The dynamic factor analysis for multivariate time series is extended. To avoid the whole estimation of state variable to each nonlinear system, we apply PF only for latent time series, and for another observed time series we use these estimated states.
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Keyword(in English) Covariance structure analysis / Large scale network / Particle filter / Latent time series
Paper # CAS2013-56,NLP2013-68
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Committee NLP
Conference Date 2013/9/19(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) State Estimation for Large Scale Nonlinear Systems by using the Covariance Structure Analysis for Multivariate Time Series and Particle Filters
Sub Title (in English)
Keyword(1) Covariance structure analysis
Keyword(2) Large scale network
Keyword(3) Particle filter
Keyword(4) Latent time series
1st Author's Name Shozo TOKINAGA
1st Author's Affiliation Graduate School of Economics, Kyushu University()
2nd Author's Name Yoshikazu IKEDA
2nd Author's Affiliation Faculty of Economics and Business administration, The University of Kitakyushu
Date 2013-09-27
Paper # CAS2013-56,NLP2013-68
Volume (vol) vol.113
Number (no) 225
Page pp.pp.-
#Pages 6
Date of Issue