The aim of this paper is to propose a convex risk measure in the framework of fuzzy random theory and verify its advantage over the conventional variance approach. For this purpose, this paper defines the quadratic deviation (QD) of fuzzy random variable as the mathematical expectation of QDs of fuzzy variables. As a result, the new risk criterion essentially describes the variation of a fuzzy ...

In this paper a network comprising alternative branching nodes with probabilistic outcomes is considered. In other words, network nodes are probabilistic with exclusive-or receiver and exclusive-or emitter. First, an analytical approach is proposed to simplify the structure of network. Then, it is assumed that the duration of activities is positive trapezoidal fuzzy number (TFN). This paper com...

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...

in this paper, the portfolio selection problem is considered, where fuzziness and randomness appear simultaneously in optimization process. since return and dividend play an important role in such problems, a new model is developed in a mixed environment by incorporating fuzzy random variable as multi-objective nonlinear model. then a novel interactive approach is proposed to determine the pref...

This paper proposes two mixed models to study a consumer’s optimal saving in the presence of two types of risk: income risk and background risk. In the first model, income risk is represented by a fuzzy number and background risk by a random variable. In the second model, income risk is represented by a random variable and background risk by a fuzzy number. For each model, three notions of prec...

We review two existing interpretations of fuzzy random variables. In the first one, the fuzzy random variable is viewed as a linguistic random variable. In the second case, it represents some incomplete knowledge about an otherwise standard random variable. For each interpretation, the information provided by the frv is described by a specific model, namely a standard probability model and a se...

A novel modeling technique for automatic control purposes is discussed. A fuzzy Markov system is proposed to describe both determined and random behavior of complex dynamic plants. The main advantage is its high computational speed. Another benefit of this method is its flexibility and applicability to both linear and nonlinear systems. A controlled Markov chain represents a fuzzy system with a...

There are random experiments in which the notion of a classical random variable, as a map sending each elementary event to a real number, does not capture their nature. This leads to fuzzy random variables in the Bugajski-Gudder sense. The idea is to admit variables sending the set Ω of elementary events not into the real numbers, but into the set M 1 (R) of all probability measures on the real...