Linear Fuzzy Differential Equations Research Articles

Stability analysis of fuzzy linear differential equations

Differential equation is a very important field of study not only in theory but in applications, and stochastic differential equation is also a significant branch of research in theory and applications. Based on the concepts of fuzzy process and fuzzy differential equation, we present some concepts of stability for fuzzy differential equations. Furthermore we propose the sufficient and necessary conditions of stability for fuzzy linear differential equations.

A method for solving nth order fuzzy linear differential equations

In this paper, an analytic method (eigenvalue–eigenvector method) for solving nth order fuzzy differential equations with fuzzy initial conditions is considered. In this method, three cases are introduced, in each case, it is shown that the solution of differential equation is a fuzzy number. In addition, the method is illustrated by solving several numerical examples.

Nth-order fuzzy linear differential equations

In this paper a numerical method for solving nth-order linear differential equations with fuzzy initial conditions is considered. The idea is based on the collocation method. The existence theorem of the fuzzy solution is considered. This method is illustrated by solving several examples.

First order linear fuzzy differential equations under generalized differentiability

First order linear fuzzy differential equations are investigated. We interpret a fuzzy differential equation by using the strongly generalized differentiability concept, because under this interpretation, we may obtain solutions which have a decreasing length of their support (which means a decreasing uncertainty). In several applications the behaviour of these solutions better reflects the behaviour of some real-world systems. Derivatives of the H-difference and the product of two functions are obtained and we provide solutions of first order linear fuzzy differential equations, using different versions of the variation of constants formula. Some examples show the rich behaviour of the solutions obtained.

A property of linear fuzzy differential equations

In this paper, we investigate a property of linear differential equations where the initial state is described by a vector of fuzzy numbers. The property is related directly to the matrix defining the original nonfuzzy system by passing to a complex number representation of the α-level sets of the fuzzy system.