Description of the Optimization Method
The optimizer calibrates the p calibration values of the maps/curves with the goal to minimize the deviation between the measured, predetermined values and the predicted n values.
Equ. 5: Optimization method
where
| p | calibration values |
| n | number of measurement points |
| Ypredicted | prediction of the function in ASCMO-MOCA/ASCMO-MOCA Runtime |
| Ymeasured | the imported data |
| Wo | Weight of Optimization |
| Wc | Weight of Constraint |
| Wg | Weight of Gradient, 1..D dimensions |
| Wk | Smoothness factors, 1..D dimensions |
The squared deviation is minimized, where the square has the effect that larger deviations are penalized even stronger.
Based on this general formula, smoothness, local constraints and gradient limits can be added. This can be expressed in the following formulas.
Smoothness
See Optimization Criterion
Gradient Limits
See Optimization Criterion
Local Constraints
