What activation function should I use for a specific regression problem?
The issue with sigmoid and tanh activations is that their gradients saturate for extreme values of their arguments. This may occur if you do not normalize your inputs. If the learned weights of the unit are such that the gradient of its activation is close to zero, it will take longer for any updates to be reflected in the unit's weights. A final layer with no non-linearity will help you scale up your hidden layers' outputs.

In the end, the performance is application specific. You should try out both kinds of activations on a subset of your data and see which performs better.