@article{ijpdea2017511,
author={Suhhiem, Mazin H.},
title={Artificial Neural Network for Solving Fuzzy Differential Equations under Generalized H ¨C Derivation},
journal={International Journal of Partial Differential Equations and Applications},
volume={5},
number={1},
pages={1--9},
year={2017},
url={http://pubs.sciepub.com/ijpdea/5/1/1},
issn={2376-9556},
abstract={The aim of this work is to present a novel approach based on the artificial neural network for finding the numerical solution of first order fuzzy differential equations under generalized H-derivation. The differentiability concept used in this paper is the generalized differentiability since a fuzzy differential equation under this differentiability can have two solutions. The fuzzy trial solution of fuzzy initial value problem is written as a sum of two parts. The first part satisfies the fuzzy condition, it contains no adjustable parameters. The second part involves feed-forward neural networks containing adjustable parameters. Under some conditions the proposed method provides numerical solutions with high accuracy.},
doi={10.12691/ijpdea-5-1-1}
publisher={Science and Education Publishing}
}