Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The suggested neural net can adjust the weights using a learning algorithm that based on the gradient descent method. The proposed method is illustrated by several examples with computer simulations.