Raheleh Jafari

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Many uncertain nonlinear systems can be modeled by linear-in-parameter models. The uncertainties can be regarded as parameter changes, which can be described as fuzzy numbers. These models are fuzzy equations. They are alternative models for uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy(More)
—————————————————————————————————— Copyright 2012 c ⃝ A. Jafarian and R. Jafari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. —————————————————————————————————— Abstract Recently,(More)
This paper build a structure of fuzzy neural network, which is well sufficient to gain a fuzzy interpolation polynomial of the form [Formula: see text] where [Formula: see text] is crisp number (for [Formula: see text], which interpolates the fuzzy data [Formula: see text]. Thus, a gradient descent algorithm is constructed to train the neural network in(More)
Partial differential equations (PDEs) emerges in modeling innumerable phenomena that applies in science and technology. In this current work, a methodology involving novel iterative technique considering feed-forward neural networks (FNNs) is suggested to extract approximate solution for the second-order nonlinear PDEs with real constant coefficients (RCCs)(More)
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