Neural network operators with hyperbolic tangent functions

We determine the global errors occurring as a result of applying the method of approximate approximations to a function defined on a compact interval. By the method of extending a function to a wider interval, we obtain upper bounds on the error estimates in the uniform norm for continuous and differentiable functions by using these approximation tools. We extend this study to the bivariate case by constructing the associated approximate approximation neural network operators.