Practical identification of NARMAX models using radial basis functions

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 co...

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Bibliographic Details
Published in:International journal of control 1990-12, Vol.52 (6), p.1327-1350
Main Authors: CHEN, S., BILLINGS, S. A., COWAN, C. F. N., GRANT, P. M.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Modelling and identification
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Summary: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.
ISSN:0020-7179
1366-5820
DOI:10.1080/00207179008953599