Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11250
Title: Improved parameter estimation from noisy time series for nonlinear dynamical systems
Authors: Nakamura, T
Hirata, Y
Judd, K
Kilminster, D
Small, M
Keywords: Gradient descent
Parameter estimation
State estimation
The least squares method
Issue Date: 2007
Publisher: World Scientific
Source: International journal of bifurcation and chaos in applied sciences and engineering, 2007, v. 17, no. 5, p. 1741-1752 How to cite?
Journal: International journal of bifurcation and chaos in applied sciences and engineering 
Abstract: 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.
URI: http://hdl.handle.net/10397/11250
ISSN: 0218-1274
EISSN: 1793-6551
DOI: 10.1142/S021812740701804X
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