Model Browser User's Guide

Setting Up the Global Model

Setting up the global model is similar to setting up the local model. You must specify the model (or curve) type and the inputs used to create the model.

Specifying the Global Model Inputs

The inputs to the global model are the variables that determine the operating point of the system being modeled. In this example, the operating point of the engine is determined by the engine's speed in revolutions per minute (rpm - often called N), load (L), and air/fuel ratio (afr).

To specify these inputs:

1. Double-click the Global Inputs icon on the model diagram.

1. The Global Input Factor Setup dialog box appears.

   

   By default there is one input to the global model. Because this engine model has three input factors, you need to increase the input factors as follows:

   1. Click the up arrow button indicated by the cursor above to increase the number of factors to three.
   2. Edit the three factors to create the engine model input. In each case, change the symbols and signals to the following:
      
      

      Symbol	Signal
      N	n
      L	load
      A	afr

   3. Leave the Min and Max boxes at the defaults (you fill them during the data selection process). You might want to set factor ranges at this stage if you were designing an experiment, but in this case there is already data available, so you use the actual range of the data to model instead.

2. Click OK to dismiss the dialog box.

Specifying the Global Model Type

Fitting the local model finds values for each model coefficient or response feature (for example, knot) for each test. These coefficients then become the data to which you fit the global model.

By default, quadratic polynomials are used to build the global model for each response feature. In this case you use the default.

To specify quadratic curves as the global model curve fitting method:

1. Double-click the icon representing the global model in the two-stage model diagram.

1. The Global Model Setup dialog box appears.

   

   1. Polynomial should already be selected from the Linear Model Subclass pop-up menu. Under Model options, the order for the three variables N, L, and A is set by default to 2, which is required.
   2. Set Stepwise to Minimize PRESS (PREdicted Sum Square error).

2. Click OK to accept the settings and dismiss the Model Settings dialog box.

You use the Stepwise feature to avoid overfitting the data; that is, you do not want to use unnecessarily complex models that "chase points" in an attempt to model random effects. Predicted error sum of squares (PRESS) is a measure of the predictive quality of a model. Min PRESS throws away terms in the model to improve its predictive quality, removing those terms that reduce the PRESS of the model.

This completes the setup of the global model.

Setting Up the Local Model

Selecting Data