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Practical identification of NARMAX models using radial basis functions. (English) Zbl 0707.93075

Summary: A wide class of discrete-time nonlinear 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-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.

MSC:

93E12 Identification in stochastic control theory
93C10 Nonlinear systems in control theory
93C55 Discrete-time control/observation systems
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References:

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