Identification of nonlinear dynamical systems from chaotic time series

The thesis mainly focuses on the problem of nonlinear dynamical system identification from observed time series, which is one of the challenging topics in nonlinear time series analysis. The problem of nonlinear dynamical system identification is addressed in this thesis in three aspects: parameter...

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Main Author:	Lu, Fangfang.
Other Authors:	Xu, Daolin
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics
Online Access:	http://hdl.handle.net/10356/6551
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Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-6551
record_format	dspace
spelling	sg-ntu-dr.10356-65512023-03-11T16:55:01Z Identification of nonlinear dynamical systems from chaotic time series Lu, Fangfang. Xu, Daolin School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics The thesis mainly focuses on the problem of nonlinear dynamical system identification from observed time series, which is one of the challenging topics in nonlinear time series analysis. The problem of nonlinear dynamical system identification is addressed in this thesis in three aspects: parameter identification; estimation of parameters and unobserved trajectory components; determination of the optimal model structure. Doctor of Philosophy (MAE) 2008-09-17T11:17:49Z 2008-09-17T11:17:49Z 2006 2006 Thesis http://hdl.handle.net/10356/6551 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics
spellingShingle	DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics Lu, Fangfang. Identification of nonlinear dynamical systems from chaotic time series
description	The thesis mainly focuses on the problem of nonlinear dynamical system identification from observed time series, which is one of the challenging topics in nonlinear time series analysis. The problem of nonlinear dynamical system identification is addressed in this thesis in three aspects: parameter identification; estimation of parameters and unobserved trajectory components; determination of the optimal model structure.
author2	Xu, Daolin
author_facet	Xu, Daolin Lu, Fangfang.
format	Theses and Dissertations
author	Lu, Fangfang.
author_sort	Lu, Fangfang.
title	Identification of nonlinear dynamical systems from chaotic time series
title_short	Identification of nonlinear dynamical systems from chaotic time series
title_full	Identification of nonlinear dynamical systems from chaotic time series
title_fullStr	Identification of nonlinear dynamical systems from chaotic time series
title_full_unstemmed	Identification of nonlinear dynamical systems from chaotic time series
title_sort	identification of nonlinear dynamical systems from chaotic time series
publishDate	2008
url	http://hdl.handle.net/10356/6551
_version_	1761781539630219264

Identification of nonlinear dynamical systems from chaotic time series

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