A Fuzzy Set Theoretic Approach to Validate Simulation Models

摘要

We develop a new approach to the validation of simulation models by exploiting elements from fuzzy set theory and machine learning. A fuzzy resemblance relation concept is used to set up a mathematical framework for measuring the degree of similarity between the input-output behavior of a simulation model and the corresponding behavior of the real system. A neuro-fuzzy inference algorithm is employed to automatically learn the required resemblance relation from real and simulated data. Ultimately, defuzzification strategies are applied to obtain a coefficient on the unit interval that characterizes the degree of model validity. An example in the airline industry illustrates the practical application of this methodology.