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Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Year: 1391
COI: JR_JACR-3-4_004
Language: EnglishView: 287
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Ahmad Jafarian - Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
Safa Measoomy Nia - Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
Raheleh Jafari - Department of Mathematics, science and research Branch, Islamic Azad University, Arak, Iran


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.


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

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Jafarian, Ahmad and Measoomy Nia, Safa and Jafari, Raheleh,1391,Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm,

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Type of center: Azad University
Paper count: 3,738
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