Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Verification and validation of simulation models
WSC '94 Proceedings of the 26th conference on Winter simulation
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Validation in Simulation: Various Positions in the Philosophy of Science
Management Science
Theory of Modelling and Simulation
Theory of Modelling and Simulation
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Simulation Modeling and Analysis
Simulation Modeling and Analysis
On (un)suitable fuzzy relations to model approximate equality
Fuzzy Sets and Systems - Theme: Basic notions
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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.