Construction and approximation rate for feedforward neural network operators with sigmoidal functions

In this article, we study the multivariate quantitative smooth approximation under differentiation of functions. The approximators here are multivariate neural network operators activated by the symmetrized and perturbed hyperbolic tangent activation function. All domains used here are infinite. The multivariate neural network operators are of quasi-interpolation type: the basic type, the Kantorovich type, and the quadrature type. We give pointwise and uniform multivariate approximations with rates. We finish with illustrations.

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Construction and approximation rate for feedforward neural network operators with sigmoidal functions

Cited by 4 publications

References 37 publications

Neural network interpolation operators based on Lagrange polynomials

Neural network interpolation operators based on Lagrange polynomials

Riemann–Liouville Fractional Integral Type Deep Neural Network Kantorovich Operators

Multivariate Smooth Symmetrized and Perturbed Hyperbolic Tangent Neural Network Approximation over Infinite Domains

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