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How to Set Up a Point-by-Point Model

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Point-by-Point Modeling Process Use Cases for Point-by-Point Models Analyzing Point-by-Point Models Exporting Point-by-Point Models to CAGE

Point-by-Point Modeling Process

Use the following process to set up a point-by-point model:

1. Create a new test plan.

   1. From the project node, click New to create a new point-by-point test plan.

   2. Select the Point-by-Point template and click OK.

   This template allows you to create point-by-point test plans with local models at each engine operating point, which is useful when testing is done at fixed operating point settings. See Use Cases for Point-by-Point Models.

   The new test plan sets up two local and two global inputs, and the local model type is set to Multiple Models. This local model type allows you to choose a variety of models to try for each test.

2. Set up the inputs by double-clicking the Inputs blocks in the test plan diagram. See Input Factor Setup.

3. Set up the multiple model types. By default, point-by-point test plan templates include four model types: a quadratic, a cubic, an RBF and a hybrid RBF. If you want to change or add to these, double-click the Local Model block in the test plan diagram, and then click Edit in the Local Model Setup dialog box. The toolbox fits all the models you choose and then indicates the best one chosen for each test in your data. For example, for some tests a radial basis function may fit best, while for others a quadratic would be acceptable. You can use any model available as one-stage models (including radial basis functions (RBF) and hybrid RBF).

   The Automatic input ranges check box defaults to selected. This selection uses the range of the data for each test, instead of a single range for all. You can choose any of the summary statistics as the selection criteria for deciding which model fits best to each test.

   For more information see Local Model Class: Multiple Models.

4. Select data to use. From the test plan node, select TestPlan > Select Data which opens the Data Selection Wizard. Load a data file (see Loading Data from File) then match up data signals with model inputs and outputs (see Step 2: Select Input Signals).

   You can click Finish on the Input Names pane: there is no need to build response models before building boundary models. Alternatively, you can continue with the Data Selection Wizard to specify any responses you want to model.

5. Create boundary models in the Boundary Editor. Select TestPlan > Boundary Models. To build point-by-point boundary models for each operating point, create a local boundary model, and select Point-by-Point for the Global evaluation type. See Creating Boundary Models.

6. If you have not already built response models, from the test plan node, double-click the Response block to choose a response and fit models.

   The view switches to the new Multiple Models node in the model tree. View and edit the local model fit for each operating point.

   Tools specific to local multiple models are described in Analyzing Point-by-Point Models.

   For details about other functionality available for viewing and refining the model fit, see Local Level and Selecting Models.

7. Export your point-by-point models to CAGE for optimized calibration. From the test plan node, select Test Plan > Export Point-by-Point Models.

   If CAGE is open, you can also use the dialog box options to automatically create the following objects from your point-by-point models:

   • Data set

   • Tradeoff

   • Optimization

   See Exporting Point-by-Point Models to CAGE.

   Note   This export option is only available if you have exactly two global inputs; otherwise you can import your point-by-point models into CAGE using the CAGE Import Tool.

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Use Cases for Point-by-Point Models

The point-by-point test plan template and the local multiple models type provides a convenient mechanism to model a number of tests at different operating points using the same set of models. Using the test plan has several advantages, including:

• You can divide the data into tests and model it within a single test plan rather than having a separate one-stage test plan for each operating point. The toolbox does not construct two-stage models or response feature models because it is impossible to choose response features that apply to all tests, when there are different model types for different tests. You must have at least one global variable (e.g. speed, injection timing, load) and you cannot use covariance modeling.

• The local multiple models type provides a smooth interface with the CAGE browser part of Model-Based Calibration toolbox software. To make use of this, you must specify two global inputs (often speed and load) which can form the axes of tradeoff tables. This useful application for multiple models allows you to calibrate from local maps.

• You can also use point-by-point models in CAGE optimization, by creating an optimization from your models, or you can use the models in an existing optimization provided the global variable values are exactly the same as the global variables used for the local models in the Model Browser.

• You can export point-by-point models to file or directly into CAGE, and automatically create an optimization, a tradeoff, and a data set from your point-by-point models.

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Analyzing Point-by-Point Models

In the local model view, you find controls and menu items specific to point-by-point models (using the Multiple models local model type).

• You can select Model > Utilities > Select Local Model to open the Model Selection window. All the alternative models are refitted at this stage (as only the best model is stored) so this refitting can take time. In the Model Selection window you can compare the fit of your local model types to the data, and select which of those model types to use for the selected test.

  In the Model Selection and Model Evaluation window, you can view local boundaries on surface and cross-section plots. If you selected Automatic input ranges during model setup, plots for point-by-point models use the data ranges per test (unlike typical two-stage models).

• You can select Model > Utilities > Add Local Model to open the Model Setup dialog box. In this dialog box, you can add a model type. When you click OK the toolbox fits the new model type to all tests, and then selects it as best if it is better (by your selection criteria) than any of the alternatives for a test. A dialog box informs you which tests (if any) have a new best model.

• You can select Model > Utilities > Summary Statistics to open the Summary Statistics dialog box. In this dialog box, you can select statistics to display in the Model Selection window and in the Diagnostic Statistics Summary Table (in the local model view). See Summary Statistics. If you are using validation data, the validation RMSE appears in this Summary Table for the test, if there is validation data for the current test (global variables must match), for comparison with the model fit RMSE. See Using Validation Data.

• In the Diagnostic Statistics pane you can select Model Selection from the drop-down list. This pane displays the value of your selection criteria (e.g., AICc) for each model type, with the best model highlighted in red. You select criteria in the Local Model Setup dialog box when you create local multiple models.

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Exporting Point-by-Point Models to CAGE

If you have exactly two global inputs, you can export point-by-point models from the Test Plan node in the Model Browser by selecting TestPlan > Export Point-by-Point Models.

• If the CAGE Browser is closed, the toolbox creates a point-by-point model tradeoff file for use with the CAGE import dialog box Import Point-by-Point Model Tradeoff. Choose a location and enter a file name in the dialog box.

• If the CAGE Browser is open, you see the Export Point-by-Point Models dialog box, as shown in the following figure.

  

You can create the following items in CAGE (depending on your check box selections in the dialog box):

• Point-by-point models, for all responses in the test plan.

  The toolbox creates a single CAGE model for each response and defines the models only at operating points corresponding to the global inputs—for example, at the values of speed and load where you performed tests.

  The toolbox uses the response name for each CAGE model name, and replaces any existing CAGE model of that name. The model inputs are connected to variables matching the symbols. The set points are the same as the first row of the dataset, corresponding to the first operating point. You can view the model surface in the Model View and the Surface Viewer.

• Local boundary models for each point-by-point model.

  If you have created local boundary models (and selected them as best), the toolbox includes them in the export.

  If you have not created local boundary models, the toolbox creates them automatically, and you see a notation of "(created)" after the text describing the boundary model. The toolbox builds a range boundary model in all local inputs for each test.

• Data set for model operating points (optional).

  The dataset contains the midpoints of the local input ranges for all tests and the global operating points.

• A point-by-point model tradeoff (optional).

  The toolbox creates a point-by-point model tradeoff in the same way as using the Import Point-by-Point Model Tradeoff dialog box. These tradeoff tables are initialized with the midpoints of the local input ranges.

• An optimization (optional).

  The toolbox creates an foptcon optimization.

  If you choose to create the optimization, use the drop-down menus to specify the response to be optimized and whether the objective should be minimized or maximized. The optimization includes the boundary model as a constraint and uses the same values as the dataset to specify a run per operating point. You can use the new optimization with Automated Tradeoff.

You can also import the point-by-point models directly into CAGE from the Model Browser using the CAGE Import Tool. If you have more than two global inputs you must use the Import Tool.

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How to Set Up a Two-Stage Model

Creating Alternative Models to Compare

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