About ASCMO-MOCA

ASCMO-MOCA is a tool for Modeling and Calibrating functions with given data. These functions consist of mathematical operations on changeable parameters, such as lookup tables. The goal is to minimize the deviation of the function's output from the given data. The function's parameters are adapted (calibrated) with an optimizer to minimize this deviation. Additional constraints, such as smoothness and gradients of curves/maps, can be considered.

The results can be visualized in different views, such as scopes and scatter plots. A residuals analysis allows to detect problems, e.g., outliers.

ASCMO-MOCA comes in two versions: the full version and the runtime version. The full version allows modeling of the function, definition of an optimization sequence, and the optimization itself. The runtime version opens existing projects from the full version, allows data import, and enables the start of the optimization, but not the definition of the function or the optimization sequence.

The building blocks of the function in ASCMO-MOCA are scalars, lookup tables, RBF(Radial Basis Function)-Nets, and models from other sources like Simulink®.

A time-independent function without inner states and loops can be directly modeled in ASCMO-MOCA. More complex, time-dependent functions are to be modeled in other tools, such as Simulink®. ASCMO-MOCA then uses the external tool during the optimization.