1. DeltaV Neural - Expert
In Expert mode, the user can select the training parameters, recommend you use the defaults for most applications

2. Right click

3. max = sqrt( number inputs )
Number Hidden Neurons
In DeltaV, number of hidden neurons is done by training the network with increasing number,
max = sqrt( number inputs )
Training stops when less than 10% reduction in error for each node used

4. Using External Historical Data
Lab data may not be collected in DeltaV, may be collected in a legacy system or recorded manually
Historical data may be saved in another system not compatible with DeltaV
DeltaV Neural is designed to use external data in development of a virtual sensor

5. Using Data Saved in a File
Sensitivity analysis and Autogeneration may be done using
the File selection

6. Specifying the Data File
You select the file as a .dat file extension

7. Data File Format Line 1 The phrase DeltaV_NN_Data <eol>
Line 2 List Input References <tab> Number of Outputs <eol>
Line 3 Number of samples in file <tab> sample rate in seconds <eol>
Line 4 An empty line <eol>
Line 5 Identifiers separated by tabs <eol>
Line 6 An empty line <eol>
Line 7 The data in the following order
Sample index <tab> output <tab> first input <tab> … <eol>
Last Line An empty line <eol>

8. Start of Example File DeltaV_NN_Data 4 1 897 6.000000
4 1
TT2-1 TT2-2 FC2-1 FC2-2

9. Example File
DeltaV_NN_Data
4 1
TT2-1 TT2-2 FC2-1 FC2-2 ## ##
Use ## to indicate a point not used. Do not remove the point!
Time shift is required to make the network converge properly!

10. Neural Network Applications Rules of Thumb
NN needs data with accurate time stamps
NN needs the correct input variables to get good correlation
Be careful that input variables are not a function of output variables
Need real, ground truth data to model (guesses don’t count)
Watch out for even “minor” changes to the process
NN is excellent at determining key input variables and the delays
But… if all input values do not have a delay, can still build a prediction model if we assume inputs with zero delay will remain constant or have minor effect on estimation!
NN can do fault diagnostics by modeling “normal” and watching for variation from “normal”

11. Neural Network Applications Rules of Thumb (Cont’d)
NNs can be used with first principle models to build more accurate Hybrid models when the first principle theory has limitations
If control problem is non-linear, NN is probably good choice
If you model a poorly controlled process, you will likely have a controller that controls poorly
Need to insure that “full operating range” is represented
Uncontrolled variability in process can cause model inaccuracies
Often need additional sensors for inferential measurement
No matter how good the NN model (technology) it is useless if there is not enough justification to implement
Although not a substitute for DOE (or structured data in general) significant results for knowledge discovery may be realized using historical data

12. Seven Deadly Sins of Neural Networks
1 - Bad Outlier Data
2 - Too Limited a Range
3 - Noisy Data
4 - Little to no Variation
5 - Too few hidden Neurons
6 - Over fitting
7 - Lack of Convergence

13. Bad Outlier Data
These bad points will cause the algorithm to shift toward them, therefore cause the whole model to be in error

14. Too Limited a Range
If the data is tightly gathered around a single value, the network will learn this gathering place rather than the whole process structure

15. Noisy Data
If the data is too noisy, the network will learn the noise and not the process

16. Little to No Variation Same as limited range
DeltaV Neural Network will exclude the data for you
If you add it, and the process does vary, the network will show a variation, but how much? How does it know?

17. Too Few a Number of Neurons
DeltaV Neural will select the number for you, this can become a problem with other programs.
The opposite can be a problem, too many neurons can cause overfitting

18. Over Fitting
Extra hidden neurons can change the weights of other neurons
Possible to have an “Offset Neuron” with a value always + or – 1
Meaning less neuron with 0 output weight
“Trim Neuron” with constant value on all but a few samples

19. Lack of Convergence
Handling outputs that have no correlation with the inputs