You may provide greater input during the generation of the neural network by selecting the Expert option from the DeltaV Neural application. If you have selected this option, additional features are available in the application. The following figure shows the application window with the Expert option selected.
Application Window -- Expert Option Selected
With the expert option selected, you can choose the data screening range by selecting a Data Screening value. This sets the limits on the data used for training based on the selected sigma (number of standard deviations) value. The default is 3.5 sigma, which means that the data training limit is Mean +/- 3.5 sigma, and values outside those limits are treated as outliers. These same limits are used during online operation of the neural net.
Also, with the Expert option selected, you can perform a Sensitivity Analysis (by clicking the Sensitivity Analysis button) to determine the sensitivity of the output to the inputs without training the neural network. After analysis is complete, the sensitivity overview appears.
Sensitivity Overview -- Expert Option Selected
The sensitivity overview with the Expert option selected contains additional buttons you use to train and verify the network. Double click a sensitivity to open a detailed view of that input sensitivity, as shown in the following figure.
Sensitivity Analysis -- Expert Option Selected
With the Expert option selected, the sensitivity detail contains more information and controls. The detail view shows the cross correlation between the input and the sampled output. The Neural application uses the peak value found for the cross correlation to determine the time delay associated with the input and its impact on the sampled value. If you are familiar with the process delay and time constants associated with the inputs, you may want to verify that your knowledge of the delay matches what was calculated. To try a different delay, enter the value in the Delay area and then click Update. In response, the sensitivity is recomputed for the delay you entered.
If the sensitivity associated with an input is low, then you may want to remove it from use in the neural network. In many cases, such inputs have already been eliminated and are shown with a red X in place of the sensitivity. To eliminate an input from the generation of the neural network, click the Use Input check box to remove the check mark and then click the Update button. If an input is shown as not selected but
you would like to examine the calculated sensitivity, select the Use Input check box and then click Update. In response, the sensitivity and cross correlation are displayed.
After you are satisfied with the inputs selected for use in the neural network, click the Train network button at the bottom of the sensitivity overview screen. You can also train a model by selecting it in the hierarchy view, right-clicking, and selecting Train Network from the pop-up menu. The Training Parameters dialog appears as shown in the following figure.
Training Parameters Dialog
The dialog contains additional options you can use to modify the neural network training. If you have expert neural network knowledge, you can change the default values to address special requirements. For example, you can change the starting number of hidden nodes used in generating the neural network to determine the impact on the model accuracy. Click OK on the dialog after you are satisfied with the values. Training begins, and another dialog appears that provides information about the training, as shown in the following figure.
Training Progress Dialog
This dialog shows the neural network training progress and results. The graph shows the training error and test error values in Engineering Units as a function of the number of epochs. As hidden nodes are added, spikes appear as a result of random initialization of the network. The right pane of the dialog shows the network with the lowest error (test error) for each hidden node number. Among these, the best network is automatically selected by the Neural application as the trained neural network model. This is highlighted in gray at the end of training and is the network that is assigned to the NN block. Note that the random initialization of the network may result in different neural network
configurations (as defined by the varying number of hidden nodes) being selected in the right pane for different training runs on the same data set.
After you train the neural network, you can verify how well the model fits the sampled data by clicking either selection in the Verify Against area at the bottom of the sensitivity overview. To verify against the data used to create the model, click Original Data. To verify against a different selection, open the Data view (select the block or Models in the hierarchy view), select a data range, return to the sensitivity overview (click the model in the hierarchy view), and then click Selected Data.
In response, a dialog appears that shows a plot of the actual values and predicted values calculated by the neural network, as shown in the following figure.
Note Only the first 65000 samples will be displayed.
Model Verification -- Actual and Predicted vs. Sample
Use the squared error value shown at the bottom of the dialog in conjunction with the plotted data to determine how accurate the neural network model is for the selected data.
To see a plot of the actual vs. predicted values for the model, click the Actual vs. Predicted radio button. A plot of the calculated vs. sampled data appears as shown below.
Model Verification -- Actual vs. Predicted Value
If the model is inaccurate, it may be the result of one or both of the following:
The model includes an input that has little or no impact on the sample parameter. This may be the case if the calculated sensitivity for the input is very low.
The model does not include an input that has a significant effect on the sample. This can happen if the input does not change over the data sample used to generate the model and is, therefore, excluded from the model.
If autogeneration has eliminated an input which you know to have a significant influence on the parameter of interest, you can add that input to those used to train the neural network by selecting Use Input from the input detail sensitivity display.
To use the new neural network definition after training, you must download the associated module. All neural network function block parameters have assigned defaults. During online operation of the function block in Control Studio, you can adjust only those parameters of the block that do not impact the neural network model file. Also, you can change some parameters that are used during controller generation in offline mode. Any changes in these parameters require that the neural network be retrained in order for the changes to be used in the neural
network model.