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A novel computational approach to approximate fuzzy interpolation polynomials

dc.contributor.authorJafarian, Ahmad
dc.contributor.authorJafari, Raheleh
dc.contributor.authorAl Qurashi, Maysaa Mohamed
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorID56389tr_TR
dc.date.accessioned2020-04-17T12:55:31Z
dc.date.available2020-04-17T12:55:31Z
dc.date.issued2016
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractThis 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.en_US
dc.description.publishedMonth8
dc.identifier.citationJafarian, Ahmad...et al. (2016). "A novel computational approach to approximate fuzzy interpolation polynomials", Springerplus, Vol. 5.en_US
dc.identifier.doi10.1186/s40064-016-3077-5
dc.identifier.issn2193-1801
dc.identifier.urihttp://hdl.handle.net/20.500.12416/3282
dc.identifier.volume5en_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing AGen_US
dc.relation.ispartofSpringerplusen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFuzzy Neural Networksen_US
dc.subjectFuzzy Interpolation Polynomialen_US
dc.subjectCost Functionen_US
dc.subjectLearning Algorithmen_US
dc.titleA novel computational approach to approximate fuzzy interpolation polynomialstr_TR
dc.titleA Novel Computational Approach To Approximate Fuzzy Interpolation Polynomialsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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