ANALYSIS OF SIMULATION-MODELS WITH FUZZY GRAPH-BASED METAMODELING

The analysis of complex simulation models using so-called metamodels o ffers reduced complexity and an understandable representation. In this paper we present an efficient algorithm that constructs a metamodel o nly from simulation data, so no a priori knowledge has to be included. Since no additional parameters have to be adjusted, the method is eas y to use. It will be shown that the resulting system approximates the underlying model with an adjustable precision. In addition, the data c an contain imprecise values or values with a corresponding confidence interval. This is especially well suited for simulation data due to it s stochastic nature. The metamodel is represented in form of a Fuzzy G raph which allows the analyst to directly extract easy to interpret if -then rules. A real world token bus model was approximated with the pr oposed method. It is shown how the resulting Fuzzy Graph can be used t o analyze this model and how the rule extraction leads to meaningful i nformation about the model behavior.