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Higher order multivariate Fuzzy approximation by basic neural network operators

dc.creatorAnastassiou, George A.
dc.date2014-10-01
dc.identifierhttps://revistas.ufro.cl/ojs/index.php/cubo/article/view/1178
dc.identifier10.4067/S0719-06462014000300003
dc.descriptionHere are studied in terms of multivariate fuzzy high approximation to the multivariate unit basic sequences of multivariate fuzzy neural network operators. These operators are multivariate fuzzy analogs of earlier studied multivariate real ones. The produced results generalize earlier real ones into the fuzzy setting. Here the high order multi- variate fuzzy pointwise convergence with rates to the multivariate fuzzy unit operator is established through multivariate fuzzy inequalities involving the multivariate fuzzy moduli of continuity of the Nth order (N ≥ 1) H-fuzzy partial derivatives, of the engaged multivariate fuzzy number valued function.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherUniversidad de La Frontera. Temuco, Chile.en-US
dc.relationhttps://revistas.ufro.cl/ojs/index.php/cubo/article/view/1178/1037
dc.sourceCUBO, A Mathematical Journal; Vol. 16 No. 3 (2014): CUBO, A Mathematical Journal; 21-35en-US
dc.sourceCUBO, A Mathematical Journal; Vol. 16 Núm. 3 (2014): CUBO, A Mathematical Journal; 21-35es-ES
dc.source0719-0646
dc.source0716-7776
dc.subjectmultivariate fuzzy real analysisen-US
dc.subjectmultivariate fuzzy neural network operatorsen-US
dc.subjecthigh order multivariate fuzzy approximationen-US
dc.subjectmultivariate fuzzy modulus of continuity and multivariate Jackson type inequalitiesen-US
dc.titleHigher order multivariate Fuzzy approximation by basic neural network operatorsen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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