Numerical Solution of Fuzzy linear Regression using Fuzzy Neural Network Based on Probability Function

Abstract

In this work, we consider the development of a fuzzy neural network based on probability function for Estimated output of fuzzy regression models with test real input and fuzzy output. The proposed approach is a fuzzification of the outputs and weights of conventional fuzzy neural network based on probability function. The error of the proposed method is based on total square error is minimized by optimization method in order to be able to obtain the optimal weights of the neural network. The advantage of the proposed approach is its simplicity and computation as well as its performance. To compare the performance of the proposed method with the other traditional methods given in the literature several numerical examples are presented.

Fully Fuzzy Neural Network For Solving Fuzzy Differential Equations

“…, where is the distance between two arbitrary fuzzy numbers." Definition ( 6), [14] where D is the distance between two arbitrary fuzzy numbers .…”

“…In 2010 researchers began using ANN for solving fuzzy differential equation such as : Effati and pakdaman in [12] ; Mosleh ,Otadi in [13] ;Ezadi ,Parandin , et al [14].…”