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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Bibliographic Details
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
Online Access:	Get full text
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