Nonlinear system identification using two-dimensional wavelet-based state-dependent parameter models

This article presents a nonlinear system identification approach that uses a two-dimensional (2-D) wavelet-based state-dependent parameter (SDP) model. In this method, differing from our previous approach, the SDP is a function with respect to two different state variables, which is realised by the...

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Bibliographic Details
Published in:International journal of systems science 2009-11, Vol.40 (11), p.1161-1180
Main Authors: Truong, Nguyen-Vu, Wang, Liuping
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Computer simulation
Control theory. Systems
Decomposition
Dynamical systems
Exact sciences and technology
identification for control
Mathematical models
Modelling and identification
multivariable nonlinearities
Nonlinear dynamics
nonlinear system identification
nonlinear systems
PRESS statistics
Series expansion
Two dimensional
Wavelet
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Summary:This article presents a nonlinear system identification approach that uses a two-dimensional (2-D) wavelet-based state-dependent parameter (SDP) model. In this method, differing from our previous approach, the SDP is a function with respect to two different state variables, which is realised by the use of a 2-D wavelet series expansion. Here, an optimised model structure selection is accomplished using a PRESS-based procedure in conjunction with orthogonal decomposition (OD) to avoid any ill-conditioning problems associated with the parameter estimation. Two simulation examples are provided to demonstrate the merits of the proposed approach.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207720902985419