Abstract

Due to deficiency of information, the probability distribution and membership functions of a fuzzy random variable
cannot be obtained explicitly. It is a challenging work to find an appropriate probability distribution and membership
function when certain partial information about a fuzzy random variable is given, such as expected value or moments.
This paper solves such problems for the maximum entropy of discrete fuzzy random variables with certain constraints. A
genetic algorithm is designed to solve the general maximum entropy model for discrete fuzzy random variables, which is
illustrated by numerical experiment.

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