The calibration aims at optimizing the unknown input parameters of a model from measuring or observation data (inputs and outputs). To perform a calibration, it is first necessary to import a data range (text file) featuring at least one input and one output. The objective is then to calibrate the other inputs and outputs on this model.
There are two type of methods to realize this calibration, each one of them hypothesizes a linearity or non linearity of the model:
- Least squares method (linear or non linear)
- Bayesian method with Gaussian hypothesis (linear or non linear)
For more information on the calibration method, refer to the following video: