Practical Identification of Narmax Models Using Radial Basis Functions.
Chen, S., Billings, S.A., Cowan, C.F.N. et al. (1 more author)
(1990)
Practical Identification of Narmax Models Using Radial Basis Functions.
Research Report.
Acse Report 393
.
Dept of Automatic Control and System Engineering. University of Sheffield
Abstract
A wide class of discrete time non-linear systems can be represented by the non-linear autoregressive moving average model with exogenous inputs or NARMAX model. This paper develops a practical algorithm for identifying NARMAX models based on radial basis functions from noise corrupted data. The algorithm consists of an iterative orthogonal-forward-regression routine coupled with model validity tests. The orthogonal-forward-regression routine selects parsimonious radial-basis-function models while the model validity tests measure the quality of fit. The modelling of a liquid level system and an automotive diesel engine are included to demonstrate the effectiveness of the identification procedure.
Metadata
Item Type:
Monograph
Authors/Creators:
Chen, S.
Billings, S.A.
Cowan, C.F.N.
Grant, P.M.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords:
Non-linear systems identification, radial basis functions, orthogonal least squares algorithms.
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