Improved parameter estimation from noisy time series for nonlinear dynamical systems

被引：7
|
作者：
Nakamura, Tomomichi
Hirata, Yoshito
Judd, Kevin
Kilminster, Devin
Small, Michael
机构：
[1] Univ Western Australia, Ctr Appl Dynam & Optimizat, Sch Math & Stat, Perth, WA 6009, Australia
[2] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Kowloon, Hong Kong, Peoples R China
[3] Univ Tokyo, Inst Ind Sci, Tokyo 1538505, Japan
来源：
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS | 2007年 / 17卷 / 05期
关键词：
gradient descent; parameter estimation; state estimation; the least squares method;
D O I：
10.1142/S021812740701804X
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：1741 / 1752
页数：12
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