PRACTICAL IDENTIFICATION OF NARMAX MODELS USING RADIAL BASIS FUNCTIONS

A wide class of discrete-time non-linear systems can be represented by the non- linear 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. © 1990 Taylor and Francis Group, LLC.