Information Sciences, ( ISI ), Volume (180), No (8), Year (2010-2) , Pages (1434-1457)

Title : ( Artificial neural network approach for solving fuzzy differential equations )

Authors: Sohrab Effati , M. Pakdaman ,

Citation: BibTeX | EndNote

The current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed-forward neural networks. First, the fuzzy differential equation is replaced by a system of ordinary differential equations. A trial solution of this system is written as a sum of two parts. The first part satisfies the initial condition and contains no adjustable parameters. The second part involves a feed-forward neural network containing adjustable parameters (the weights). Hence by construction, the initial condition is satisfied and the network is trained to satisfy the differential equations. This method, in comparison with existing numerical methods, shows that the use of neural networks provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples

Keywords

Fuzzy differential equations Fuzzy Cauchy problem Artificial neural
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@article{paperid:1014439,
author = {Effati, Sohrab and M. Pakdaman},
title = {Artificial neural network approach for solving fuzzy differential equations},
journal = {Information Sciences},
year = {2010},
volume = {180},
number = {8},
month = {February},
issn = {0020-0255},
pages = {1434--1457},
numpages = {23},
keywords = {Fuzzy differential equations Fuzzy Cauchy problem Artificial neural networks},
}

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%0 Journal Article
%T Artificial neural network approach for solving fuzzy differential equations
%A Effati, Sohrab
%A M. Pakdaman
%J Information Sciences
%@ 0020-0255
%D 2010

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