1. Download the NN function block to a DeltaV controller.
2. Verify that the transmitters that provide the inputs are working correctly.
3. Make sure the DeltaV Historian is enabled and that the area containing the module has been assigned to the DeltaV Historian. 4. Download the system.
5. Verify all the inputs and outputs of the NN function block.
6. Open the Neural application to commission the NN control. You can open the Neural application from Control Studio or DeltaV Explorer by right-clicking the NN function block and selecting Neural either from the drop-down menu or from the DeltaV Advanced Control menu.
DeltaV Neural Application
The referenced inputs (REF) parameters and the sampled (SAMPLE) parameter are trended automatically. Up to six of the neural network inputs can be trended at one time. To add or remove a parameter from the trend view, double-click the parameter in the Operation Trend pane. When you right-click a selected parameter, options are provided that allow you to modify the trend view (that is, change the trend range). You can use the toolbar controls or the slider bar to adjust the time window that is shown on the trend. Note: If the trend parameter list and trend view shows any inputs with an incorrect value of 0.0, it may be a result of configuring invalid reference inputs in the NN block. Refer to the Neural Network (NN) Function Block topic for more information on correcting this and then try again.
Next, you need to enter a value for the Sample Multiplier to configure the Time to Steady State (TSS) - the estimated time (in seconds) for the process to completely respond to changes in inputs. This field also corresponds to the horizon over which DeltaV Neural estimates input delays.
The Time to Steady State (TSS) is calculated as: TSS = 50 * Data Sampling Rate * Sample Multiplier.
The Data Sampling Rate (DSR) is the Historian Sampling rate entered in the NN function block Properties dialog. If you are using data from a file, the sampling rate in the file is read as the Data Sampling Rate for the TSS calculation.
The Sample Multiplier is the factor at which data from the given data set (created by sampling at the Data Sampling Rate) is sampled for creating the neural net model. For example, Sample Multiplier of one (1) means all the samples at the DSR are used, while Sample Multiplier of two (2) implies that only every second sample from the data set is going to be used for training - half of the former case. In effect, the product of DSR and Sample Multiplier establishes the effective sampling rate of the data to be used for training.
To modify the TSS, edit the Sample Multiplier according to the formula above. For example, if Historian Sampling Rate is one second and the process TSS is estimated to be 100 seconds, set the Sample Multiplier to two (2). If you are using data from a file in which the sampling rate is two seconds, the minimum possible TSS is 100 seconds (Sample Multiplier = 1). Other possible TSS values are 200 seconds, 300 seconds, and so on for Sample Multipliers of 2, 3, and so on.
When using the NN block in conjunction with an LE block for lab analysis samples, note that the TSS value establishes the maximum sample delay that affects the NN block's automatic correction mechanism.
To form the data set to train the neural network, all input and output data should show reasonable variation (flat data lines have no effect) about the normal region of operation of the process. There should be sufficient data reflecting normal process operating conditions. If the time between samples is large, the data must be collected over a greater time. Generally speaking, the larger the data set, the better the resulting trained neural net. Also, NNs with a higher number of inputs require more data than smaller NNs. Once sufficient data is collected, select the
historical data to generate the neural network. The green area on the trend view indicates the data used to generate the neural network. You can adjust the range by dragging the start and end bars or by right-clicking the trend to set the right or left edge of the green area. If necessary, you can exclude bad data within the selected data by right-clicking within the green and stretching the red band over the bad data. After you select the data, click Autogenerate to create a neural network for the process.
Note The module that contains the NN function block should not be open in Control Studio when you request Autogenerate because the NN
function block is updated in the DeltaV database during autogenerate.
Note It is recommended that system time adjustments are not made over the data collection period. If it is necessary to make system time
adjustments, it is best to exclude the data over the time range.
While the Neural application is generating the neural network, a line of status information appears in the Autogeneration area of the interface to show the progress.
When the minimum error is achieved, a pop-up message appears saying that the training is completed. The trained network is loaded into the NN function block in the database. The neural network model that is created is shown automatically in the left panel of the DeltaV Neural screen. A model overview of the calculated sensitivity is automatically displayed.
Sensitivities View
The sensitivities indicate how changes in the inputs affect the sampled measurement that is modeled by the neural network. Inputs that were determined to have insignificant influence on the sampled value are not used in the neural network and are indicated in the sensitivity display by a large red X shown in place of the sensitivity plot.
Sensitivity Detail
The vertical scale shows the sensitivity of the output to this input. (Note that the sum of the sensitivities for all inputs is 1.0.) The time scale shows the delay (the time it takes for a change in the input to be reflected in the output). The average sensitivity is shown as well.
Once you have viewed the input sensitivities and are satisfied that the important inputs were selected, download the associated module to utilize the trained network. If you would like input in the training of the neural network or more details on how well the neural network fits a set of process data, use the expert features of DeltaV Neural, as detailed in the Expert Option topic.