Presentation 2009-08-03
State Estimation by using Particle Filters based on Equation Approximations with the Genetic Programming and its Applications to Suppression of Fluctuations of Time Series
Kangrong TAN, Shozo TOKINAGA,
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Abstract(in English) This report deals with the state estimation by using particle filters (PF) based on equation approximations with the Genetic Programming (GP) and its applications to suppression of fluctuations of time series. In real markets, it is necessary to identify state equations as well as states themselves. We assume each individual in the pool of GP method as a state equation in PFs, and then apply the improvement of functional approximation based on the fitness of individuals obtained from the likelihood in PFs. Simulation studies for the analysis of artificial and real markets are shown.
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Keyword(in English) Agents' behavior / Particle filter / Genetic Programming / State estimation / Nonlinear modeling / Time series
Paper # NLP2009-48
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Committee NLP
Conference Date 2009/7/27(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 by using Particle Filters based on Equation Approximations with the Genetic Programming and its Applications to Suppression of Fluctuations of Time Series
Sub Title (in English)
Keyword(1) Agents' behavior
Keyword(2) Particle filter
Keyword(3) Genetic Programming
Keyword(4) State estimation
Keyword(5) Nonlinear modeling
Keyword(6) Time series
1st Author's Name Kangrong TAN
1st Author's Affiliation Faculty of Economics, Kurume University()
2nd Author's Name Shozo TOKINAGA
2nd Author's Affiliation Graduate School of Economics, Kyushu University
Date 2009-08-03
Paper # NLP2009-48
Volume (vol) vol.109
Number (no) 167
Page pp.pp.-
#Pages 6
Date of Issue