Improved parameter estimation from noisy time series for nonlinear dynamical systems

T. Nakamura, Y. Hirata, Kevin Judd, D.J. Kilminster, Michael Small

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1741-1752
JournalInternational Journal of Bifurcation and Chaos
Volume17
Issue number5
DOIs
Publication statusPublished - 2007

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