A fuzzy set theoretic approach to validate simulation models

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A fuzzy set theoretic approach to validate simulation models

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ACM Transactions on Modeling and Computer Simulation (TOMACS) archive
Volume 16 , Issue 4 (October 2006) table of contents

Pages: 375 - 398

Year of Publication: 2006

ISSN:1049-3301

Authors

Jurgen Martens	Catholic University of Leuven, Leuven, Belgium
Ferdi Put	Catholic University of Leuven, Leuven, Belgium
Etienne Kerre	Ghent University, Ghent, Belgium

Publisher

ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

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