Higher Order Multivariate Fuzzy Approximation by basic Neural Network Operators

• George A Anastassiou ^[1]
  1. [1] University of Memphis Department of Mathematical Sciences
• Localización: Cubo: A Mathematical Journal, ISSN 0716-7776, ISSN-e 0719-0646, Vol. 16, Nº. 3, 2014, págs. 21-35
• Idioma: inglés
• DOI: 10.4067/S0719-06462014000300003
• Enlaces
  • Texto completo
• Resumen
  • español

    Utilizando aproximaciones multivariadas difusas superiores, estudiamos la aplicación a secuencias basicas unitarias multivariadas de operadores de redes neuronales disfusas multivariadas. Estos operadores son analogos difusos multivariados de los reales multi-variados estudiados anteriormente. Los resultados obtenidos generalizan los resultados reales anteriores en el marco difuso. La convergencia puntual difusa multivariada de orden superior con velocidades para los operadores unitarios difusos multivariados se establece a travóes de desigualdades difusas multivariadas que involucran los móodulos de continuidad difusos multivariados de las derivadas parciales H-difusas de N-ésimo orden ( N 1 ) de las funciones con valores numóericos difusos multivariados

  • English

    Here 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.

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