General Dual Fuzzy Linear Systems

Abstract This paper mainly intends to discuss the solution of fully fuzzy linear systems (FFLS) Ax + b = Cx + d, where A and C are fuzzy matrices, b and d are fuzzy vectors. We transform the systems by using fuzzy numbers with a new parametric form for finding a fuzzy vector x that satisfies in the system

A New Method for Solving General Dual Fuzzy Linear Systems

. According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS) cannot be replaced by a fuzzy linear system (FLS). In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derive

Existence of Solution of Nonlinear Fuzzy Fredholm Integro-differential Equations

In this paper, we prove some results concerning the existence of solution of a class of nonlinear fuzzy Fredholm integro-differential equations. Also an iterative approach is proposed to obtain approximate solution of a class of nonlinear fuzzy Fredholm integro-differential equation of the second kind. A numerical example is presented to illustrate the proposed method

Solving the Second Order Fuzzy Differential Equations by Fuzzy Neural Network

In this paper, we interpret a two-point initial value problem for a second order fuzzy differential equation. We investigate a problem of finding a numerical approximation of the solution by using fuzzy neural network. Here neural network is considered as a part of a larger field called neural computing or soft computing. Finally, we illustrate our approach on an applied example in engineering

Fuzzy neural network approach to fuzzy polynomial

In this paper, an architecture of fuzzy neural networks is proposed to find a real root of a dual fuzzy polynomial (if exists) by introducing a learning algorithm. We proposed a learning algorithm from the cost function for adjusting of crisp weights. According to fuzzy arithmetic, dual fuzzy polynomials can not be replaced by a fuzzy polynomials, directly. Finally, we illustrate our approach by numerical examples

Solving Fuzzy Linear System by Fuzzy Neural Network and Applications in Economics

In this paper, a novel hybrid method based on fuzzy neural network for estimate fuzzy coefficients (parameters) of fuzzy linear supply and demand function, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed

DECOMPOSITION METHOD FOR SOLVING FULLY FUZZY LINEAR SYSTEMS

In this paper, we investigate the existence of a positive solution of fully fuzzy linear equation systems. This paper mainly to discuss a new decomposition of a nonsingular fuzzy matrix, the symmetric times triangular (ST) decomposition. By this decomposition, every nonsingular fuzzy matrix can be represented as a product of a fuzzy symmetric matrix S and a fuzzy triangular matrix T

Canonical representation for approximating solution of fuzzy polynomial equations

In this paper, the concept of canonical representation is proposed to find fuzzy roots of fuzzy polynomial equations. We transform fuzzy polynomial equations to system of crisp polynomial equations, this transformation is perform by using canonical representation based on three parameters Value, Ambiguity and Fuzziness

Canonical representation for approximating solution of fuzzy polynomial equations

In this paper, the concept of canonical representation is proposed to find fuzzy roots of fuzzy polynomial equations. We transform fuzzy polynomial equations to system of crisp polynomial equations, this transformation is perform by using canonical representation based on three parameters Value, Ambiguity and Fuzziness