in this paper, we study the finitely many constraints of fuzzy relation inequalities problem and optimize the linear objective function on this region which is defined with fuzzy max-lukasiewicz operator. in fact lukasiewicz t-norm is one of the four basic t-norms. a new simplification technique is given to accelerate the resolution of the problem by removing the components having no effect on ...

System of linear equations is a problem that may be solved for solving many problems in various areas of applied sciences. Fuzzy methods constitute an important mathematical and computational tool for modeling real-world systems with uncertainties of parameters. The paper discusses fully fuzzy linear systems (shown as FFLS) using embedding approach to find its non-zero fuzzy number solutions. W...

we present a model and propose an approach to compute an approximate solution of fully fuzzy linear system $(ffls)$ of equations in which all the components of the coefficient matrix are either nonnegative or nonpositive. first, in discussing an $ffls$ with a nonnegative coefficient matrix, we consider an equivalent $ffls$ by using an appropriate permutation to simplify fuzzy multiplications. t...

0

In this work, we propose a simple method for obtaining the algebraic solution of a complex interval linear system where coefficient matrix is a complex matrix and the right-hand-side vector is a complex interval vector. We first use a complex interval version of the Doolittle decomposition method and then we restrict the Doolittle's solution, by complex limiting factors, to achieve a complex in...

In previous studies we first concentrated on utilizing crisp simulationto produce discrete event fuzzy systems simulations. Then we extendedthis research to the simulation of continuous fuzzy systems models. In this paperwe continue our study of continuous fuzzy systems using crisp continuoussimulation. Consider a crisp continuous system whose evolution depends ondifferential equations. Such a ...

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...