Neural Network Approach to Solving Fuzzy Nonlinear Equations using Z-Numbers

Abstract

In this article, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this article, the fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to the uncertain system is to obtain the Z-number coefficients and variables of the fuzzy equation. Nevertheless, it is extremely hard to get the Z-number coefficients of the fuzzy equations. In this article, in order to model the uncertain nonlinear systems, a novel structure of the multilayer neural network is utilized in such a manner that it is able to obtain the Z-number coefficients of the fuzzy equation. The suggested technique is validated by some examples with applications.