Joint Maximum a Posteriori Smoother for State and Parameter Estimation in Nonlinear Dynamical Systems*

2012 ◽  
Vol 45 (16) ◽  
pp. 900-905 ◽  
Author(s):  
Dimas A. Dutra ◽  
Bruno O.S. Teixeira ◽  
Luis A. Aguirre
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Nonlinear Dynamical

Parameter estimation of nonlinear dynamical systems based on integrator theory

2009 ◽  
Vol 19 (3) ◽  
pp. 033130 ◽  
Author(s):  
Haipeng Peng ◽  
Lixiang Li ◽  
Yixian Yang ◽  
Cong Wang
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical

scholarly journals Optimal Initial State for Fast Parameter Estimation in Nonlinear Dynamical Systems

2015 ◽  
Vol 48 (20) ◽  
pp. 557-562
Author(s):  
Qiaochu Li ◽  
Carine Jauberthie ◽  
Lilianne Denis-vidal ◽  
Zohra Cherfi
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Initial State ◽  
Nonlinear Dynamical

Persistently-exciting signal generation for Optimal Parameter Estimation of constrained nonlinear dynamical systems

2018 ◽  
Vol 77 ◽  
pp. 231-241 ◽  
Author(s):  
Leonardo M. Honório ◽  
Exuperry Barros Costa ◽  
Edimar J. Oliveira ◽  
Daniel de Almeida Fernandes ◽  
Antonio Paulo G.M. Moreira
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Optimal Parameter ◽  
Signal Generation ◽  
Nonlinear Dynamical ◽  
Optimal Parameter Estimation

IMPROVED PARAMETER ESTIMATION FROM NOISY TIME SERIES FOR NONLINEAR DYNAMICAL SYSTEMS

2007 ◽  
Vol 17 (05) ◽  
pp. 1741-1752 ◽  
Author(s):  
TOMOMICHI NAKAMURA ◽  
YOSHITO HIRATA ◽  
KEVIN JUDD ◽  
DEVIN KILMINSTER ◽  
MICHAEL SMALL
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Dynamical Systems ◽  
Least Squares ◽  
Least Squares Method ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical System ◽  
Parameter Estimates ◽  
Nonlinear Dynamical ◽  
Observational Noise

In this paper we consider the problem of estimating the parameters of a nonlinear dynamical system given a finite time series of observations that are contaminated by observational noise. The least squares method is a standard method for parameter estimation, but for nonlinear dynamical systems it is well known that the least squares method can result in biased estimates, especially when the noise is significant relative to the nonlinearity. In this paper, it is demonstrated that by combining nonlinear noise reduction and least squares parameter fitting it is possible to obtain more accurate parameter estimates.

scholarly journals Optimal input design for parameter estimation in a bounded-error context for nonlinear dynamical systems

Automatica ◽  
2018 ◽  
Vol 92 ◽  
pp. 86-91 ◽  
Author(s):  
Carine Jauberthie ◽  
Lilianne Denis-Vidal ◽  
Qiaochu Li ◽  
Zohra Cherfi-Boulanger
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical ◽  
Optimal Input ◽  
Input Design ◽  
Bounded Error ◽  
Optimal Input Design
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