Product overview
The Optimal Experimental Design Toolbox allows to optimize model parameters and corresponding measurement conditions.
Contents
Introduction
Often models contain roughly known model parameters, which should to be determined more accurately. These parameters can be optimized, so that the model is as consistent as possible with results of measurements. These measurement results are obtained under different measurement conditions, which crucially affect the information content of the measurement results. These measurement conditions can also be optimized so that the information contain of the corresponding measurement results is maximized. As a result fewer measurements have to be carried out to gain sufficient accurate model parameters which saves time, money and effort.
Optimize measurement conditions
You only have to specify the model function, the selectable measurements and the variances of the corresponding measurement errors. Than the Optimal Experimental Design Toolbox can calculate which measurements you have to use to be able to determine the parameters optimally. Optimally here means that the expected error, which will be done by determining the parameters, is minimal.
Constrain the measurements
In addition it is possible to define linear constraints to the measurement points. Thus, for example, the total cost of the measurement that will be chosen can be limited. A simple constraint, for example, can be a maximal number of measurements.
Take previous measurements into account
If for the modeled value measurement results already exist, the corresponding measurements and variances can also be considered. The Optimal Experimental Design Toolbox can tell you how great the benefit of additional measurements would be and thereby, if further measurements are necessary.
Optimize model parameters
Furthermore the Optimal Experimental Design Toolbox can optimize the parameters of models by means of accomplished measurements. Box constraints on the model parameters can be considered in the optimization.