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 mainlyintends to offer a novel method for finding a solution of a fuzzy equation thatsupposedly has a real solution. For this scope, we applied an architecture offuzzy neural networks such that the corresponding connection weights are realnumbers. The suggested neural net can adjust the weights using a learningalgorithm that based on the gradient descent method. The proposed method isillustrated by several examples with computer simulations.

کلیدواژه ها:

Fuzzy equations ، Fuzzy feed-forward neural network (FFNN) ، Cost function ، Learning algorithm