USE OF PRESS FOR DISTURBANCE MODEL SELECTION

In the areas of process identification (Ljung, 1987), optimal stochastic controller design (Box and Jenkins, 1976) and controller performance assessment (Harris, 1989), it is often desirable or necessary to determine a model 

We begin by replacing by another transfer function of the form 

Equation (3.33) can be written in the following linear regression form 

The least squares estimator and the PRESS statistic are used here as a new way to estimate the best disturbance model in Equation (3.35). This is an ideal application because the objective is to choose the most parsimonious model (smallest m) while, at the same time, achieving a good approximation in Equation (3.34). An indication that a sufficient model order has been chosen is whether the residuals associated with the model 

The following simulation examples are used to illustrate the application of this approach to disturbance modelling. 

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