Statistical Learning Theory is commonly regarded as a sound framework within which we handle a variety of learning problems in presence of small size data samples. It has become a rapidly progressing research area in machine learning. The theory is based on real random samples and as such is not ready to deal with the statistical learning problems involving complex fuzzy random samples, which w...

The concept of (fuzzy) probability density function of fuzzy random variable is proposed in this paper. Due to the "resolution identity", we can construct a closed fuzzy number from a family of closed intervals. Using the same technique, we can construct the (fuzzy) probability density function of fuzzy random variable from the known probability density function. The basic idea of the new metho...

In practical signal detection scenarios, parameters of a random process are often uncertain. In this paper, we model such uncertainties as fuzzy parameters of a stationary random process. A fuzzy Neyman-Pearson hypothesis test concept which accepts any number of fuzzy parameters is presented. A suitable decision rule is developed by applying theory for ordering fuzzy numbers, and stated in term...

In this paper, we focus on multiobjective fuzzy random programming problems with simple recourse through a fractile optimization model, in which fuzzy random variables coefficients are involved in equality constraints, and random variables coefficients are involved in the objective functions. In the proposed method, equality constraints with fuzzy random variables are defined on the basis of a ...

A novel approach is proposed for the problem of testing statistical hypotheses about the fuzzy mean of a fuzzy random variable.The concept of the (uniformly) most powerful test is extended to the (uniformly) most powerful fuzzy-valued test in which the test function is a fuzzy set representing the degrees of rejection and acceptance of the hypothesis of interest.For this purpose, the concepts o...