q-DEFORMED AND -PARAMETRIZED A-GENERALIZED LOGISTIC FUNCTION BASED COMPLEX VALUED TRIGONOMETRIC AND HYPERBOLIC NEURAL NETWORK HIGH ORDER APPROXIMATIONS

Here we research the univariate quantitative approximation of complex valued continuous functions on a compact interval by complex valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function's high order derivatives. The nature of our approximations are trigonometric and hyperbolic. Our operators are defined by using a density function generated by a q-deformed and lambda-parametrized A-generalized logistic function, which is a sigmoid function. The approximations are pointwise and of the uniform norm. The related complex valued feed-forward neural networks are with one hidden layer.