Jafarian Ahmad, Jafari Raheleh, Mohamed Al Qurashi Maysaa, Baleanu Dumitru
Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran.
Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico.
Springerplus. 2016 Aug 27;5(1):1428. doi: 10.1186/s40064-016-3077-5. eCollection 2016.
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 such a way that the unknown coefficients of fuzzy polynomial are estimated by the neural network. The numeral experimentations portray that the present interpolation methodology is reliable and efficient.
本文构建了一种模糊神经网络结构，该结构足以获得形如[公式：见正文]的模糊插值多项式，其中[公式：见正文]为清晰数（对于[公式：见正文]），它对模糊数据[公式：见正文]进行插值。因此，构造了一种梯度下降算法来训练神经网络，以便通过神经网络估计模糊多项式的未知系数。数值实验表明，目前的插值方法是可靠且有效的。
