in applications there occur different forms of uncertainty. the twomost important types are randomness (stochastic variability) and imprecision(fuzziness). in modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. however, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.since many years t...

Fuzzy systems can provide us with universal approximation models of deterministic input–output relationships, but in the stochastic environment few achievements related to the subject have so far achieved. In the paper a novel stochastic Takagi–Sugeno (T–S) fuzzy system is introduced to represent approximately existing randomness in many real-world systems. By recapitulating the general archite...

In the present paper Fuzzy process, similar to a stochastic process is carried out for reliability analysis of the network modeling. Classical reliability analysis carries out the probability and binary state assumption which has been found to be inadequate to handle uncertainties of failure data and modeling. In the present paper we have attempted to review the fuzzy tools when dealing with re...

This paper considers systems whose input signals are fuzzy stochastic processes of second order. The analysis is entirely restricted to discrete time linear time-invariant systems. Convergence conditions of the output are given. The equations on the mean value functions and the covariance functions are derived. The representation of fuzzy stochastic processes is also discussed. c © 2002 Elsevie...

This paper discusses hybrid probabilistic and fuzzy set approaches to propagating randomness and imprecision in risk assessment and fuzzy time series models. Stochastic and Computational Intelligence methods, such as Probability bounds analysis, Fuzzy -levels analysis, Fuzzy random vectors, Wavelets decomposition and Wavelets Networks are combined to capture different kinds of uncertainty. The...

this paper presents a mathematical model for a flow shop scheduling problem consisting of m machine and n jobs with fuzzy processing times that can be estimated as independent stochastic or fuzzy numbers. in the traditional flow shop scheduling problem, the typical objective is to minimize the makespan). however,, two significant criteria for each schedule in stochastic models are: expectable m...

background one of most important operations research problems is nurse scheduling problem (nsp) that tries to find an optimal way to assign nurses to shifts with a set of hard constraints. most of the researches are dealing with this problem in deterministic environment with constant parameters. while in the real world applications of nsp, the stochastic nature of some parameters like number of...

The aim of this paper is to price European options for underlying assets with stochastic volatility (SV) in Heston model in 1993 using fuzzy set theory. The main idea is to transform the probability distribution of stochastic volatility to its possibility distribution (from ‘volatility smile to volatility frown’) and reduce the problem to a fuzzy stochastic process for underlying asset with a n...