A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of...

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Bibliographic Details
Published in:Automatica (Oxford) 2006-07, Vol.42 (7), p.1189-1197
Main Authors: Li, Kang, Peng, Jian-Xun, Bai, Er-Wei
Format: Article
Language:English
Subjects:
Applied sciences
Computational complexity
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Forward model selection
Linear-in-the-parameters model
Model compactness
Model refinement
Model structure selection
Modelling and identification
Nonlinear system
System identification
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Summary:This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2006.03.004