Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
The approximation operators with sigmoidal functions
Computers & Mathematics with Applications
Fractional Differentiation Inequalities
Fractional Differentiation Inequalities
Multivariate hyperbolic tangent neural network approximation
Computers & Mathematics with Applications
Multivariate sigmoidal neural network approximation
Neural Networks
Univariate hyperbolic tangent neural network approximation
Mathematical and Computer Modelling: An International Journal
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Here, we study the univariate fractional quantitative approximation of real valued functions on a compact interval by quasi-interpolation sigmoidal and hyperbolic tangent neural network operators. These approximations are derived by establishing Jackson type inequalities involving the moduli of continuity of the right and left Caputo fractional derivatives of the engaged function. The approximations are pointwise and with respect to the uniform norm. The related feed-forward neural networks are with one hidden layer. Our fractional approximation results into higher order converges better than the ordinary ones.