Robust identification for linear-in-the-parameters modelsHong, X., Harris, C. J., Chen, S. and Sharkey, P. M. (2003) Robust identification for linear-in-the-parameters models. In: Ruano, A. E., Ruano, M. G. and Fleming, P. J. (eds.) Intelligent Control Systems and Signal Processing 2003. Ifac Proceedings Series. Pergamon-Elsevier Science Ltd, Kidlington, pp. 273-278. ISBN 0080440886 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryIn this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally efficient. A numerical example is included to demonstrate effectiveness of the algorithms. Copyright (C) 2003 IFAC.
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