No 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.

In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier.

Validation is one of the most important steps in developing a reliable simulation model. It evaluates whether or not the model forms a representation of the simulated system accurate enough to satisfy the goals of the modelling study. The methods that are currently available for model validation are binary in nature in the sense that they only allow either to accept or reject the validity of the model. In this paper, we develop a new, fuzzy set theoretic method that allows to express degrees of model validity and that is hence continuous in nature. The method employs a fuzzy-neural machine learning algorithm and makes use of a new concept in fuzzy set theory known as resemblance relations. By a computational experiment, we demonstrate how our method can be used to discriminate more from less valid simulation models of a particular manufacturing process.

No abstract