Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
The approximation operators with sigmoidal functions
Computers & Mathematics with Applications
Fractional neural network approximation
Computers & Mathematics with Applications
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Here we study the multivariate quantitative approximation of real and complex valued continuous multivariate functions on a box or R^N, 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.