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A novel computational approach to approximate fuzzy interpolation polynomials

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dc.contributor.author Jafarian, Ahmad
dc.contributor.author Jafari, Raheleh
dc.contributor.author Al Qurashi, Maysaa Mohamed
dc.contributor.author Baleanu, Dumitru
dc.date.accessioned 2020-04-17T12:55:31Z
dc.date.available 2020-04-17T12:55:31Z
dc.date.issued 2016-08-27
dc.identifier.citation Jafarian, Ahmad...et al. (2016). "A novel computational approach to approximate fuzzy interpolation polynomials", Springerplus, Vol. 5. tr_TR
dc.identifier.issn 2193-1801
dc.identifier.uri http://hdl.handle.net/20.500.12416/3282
dc.description.abstract This paper build a structure of fuzzy neural network, which is well sufficient to gain a fuzzy interpolation polynomial of the form y(p) = a(n)x(p)(n) +... + a(1)x(p) + a(0) where a(j) is crisp number (for j = 0,..., n), which interpolates the fuzzy data (x(j), y(j)) (for j = 0,..., n). 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. tr_TR
dc.language.iso eng tr_TR
dc.publisher Springer International Publishing AG tr_TR
dc.relation.isversionof 10.1186/s40064-016-3077-5 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Fuzzy Neural Networks tr_TR
dc.subject Fuzzy Interpolation Polynomial tr_TR
dc.subject Cost Function tr_TR
dc.subject Learning Algorithm tr_TR
dc.title A novel computational approach to approximate fuzzy interpolation polynomials tr_TR
dc.type article tr_TR
dc.relation.journal Springerplus tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 5 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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