A class of fuzzy random optimization: Expected value models.
The authors introduce (possibilistic) fuzzy variables and their real-valued expectation which is some kind of defuzzification of the possibility distribution. A fuzzy random variable \(Y\) is a measurable mapping from a probability space to a collection of fuzzy variables. The expected expectation of these fuzzy variables figures as a (scalar) expectation of \(Y\) (which is obviously different from the well-known fuzzy-valued Aumann-expectation). Using this expectation, the authors present three types of optimization models which can be solved by simulation, neural networks or genetic algorithms.
scientific article; zbMATH DE number 2057903