A fast nonlinear model identification method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem r...

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Published in:	IEEE transactions on automatic control Vol. 50; no. 8; pp. 1211 - 1216
Main Authors:	Kang Li, Jian-Xun Peng, Irwin, G.W.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.08.2005 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Applied sciences Computational complexity Computer science; control theory; systems Control theory. Systems Exact sciences and technology fast recursive algorithm Least squares methods Matrix decomposition Modelling and identification Nonlinear dynamical systems nonlinear system identification Nonlinear systems Numerical stability Parameter estimation System identification US Department of Transportation nonlinear system identification Model matching Identification System identification Dynamical system Fast algorithm Non linear system Recursive algorithm Computational complexity fast recursive algorithm Numerical stability
ISSN:	0018-9286, 1558-2523
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Abstract	The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
AbstractList	The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
Author	Kang Li Irwin, G.W. Jian-Xun Peng
Author_xml	– sequence: 1 surname: Kang Li fullname: Kang Li organization: Sch. of Electr. & Electron. Eng., Queen's Univ. of Belfast, UK – sequence: 2 surname: Jian-Xun Peng fullname: Jian-Xun Peng organization: Sch. of Electr. & Electron. Eng., Queen's Univ. of Belfast, UK – sequence: 3 givenname: G.W. surname: Irwin fullname: Irwin, G.W. organization: Sch. of Electr. & Electron. Eng., Queen's Univ. of Belfast, UK
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SubjectTerms	Algorithm design and analysis Applied sciences Computational complexity Computer science; control theory; systems Control theory. Systems Exact sciences and technology fast recursive algorithm Least squares methods Matrix decomposition Modelling and identification Nonlinear dynamical systems nonlinear system identification Nonlinear systems Numerical stability Parameter estimation System identification US Department of Transportation
Title	A fast nonlinear model identification method
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