Abstract
This paper build a structure of fuzzy neural network, which is well sufficient to gain a fuzzy interpolation polynomial of the form
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\begin{document}$$y_{p}=a_{n}x_{p}^n+ \cdots +a_{1}x_{p}+a_{0}$$\end{document}
y
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where
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\begin{document}$$a_{j}$$\end{document}
a
j
is crisp number (for
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\begin{document}$$j=0,\ldots ,n)$$\end{document}
j
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,
n
)
, which interpolates the fuzzy data
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\begin{document}$$(x_{j},y_{j})\,(for\,j=0,\ldots ,n)$$\end{document}
(
x
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y
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(
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. 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.