Practical identification of NARMAX models using radial basis functions
International Journal of ControlPublished 1 December 1990
Sheng Chen, S.A. Billings, C.F.N. Cowan, P.M. Grant
Citations303
SJR quartileQ2
SJR score0.68
SNIP0.90
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TL;DR
A practical algorithm for identifying NARMAX models based on radial basis functions from noise-corrupted data is developed, consisting of an iterative orthogonal-forward-regression routine coupled with model validity tests.
Abstract
A wide class of discrete-time non-linear systems can be represented by the nonlinear autoregressive moving average (NARMAX) model with exogenous inputs. 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-basisTunc-tion 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.
Keywords
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