Multivariate hyperbolic tangent neural network approximation

Abstract

Here we study the multivariate quantitative approximation of real and complex valued continuous multivariate functions on a box or RN, N∈N, by the multivariate quasi-interpolation hyperbolic tangent neural network operators. This approximation is derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order partial derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the hyperbolic tangent function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer.