A novel computational approach to approximate fuzzy interpolation polynomials.
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.
Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran.
Ahmad
A
Jafarian
Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico.
Raheleh
R
Jafari
Department of Mathematics, King Saud University, Riyadh, 11495 Saudi Arabia.
Maysaa
M
Mohamed Al Qurashi
Department of Mathematics, Faculty of Art and Sciences, Cankaya University, 06530 Balgat, Ankara, Turkey ; Institute of Space Sciences, Magurele-Bucharest, Romania.
Dumitru
D
Baleanu
Cost function; Fuzzy interpolation polynomial; Fuzzy neural networks; Learning algorithm