On the approximation of functions by tanh neural networks

We derive bounds on the error, in high-order Sobolev norms, incurred approximation of Sobolev-regular as well analytic functions by neural networks with hyperbolic tangent activation function. These provide explicit estimates error respect to size networks. show that tanh only two hidden layers suffice approximate at comparable or better rates than much deeper ReLU