We implement an architecture with explicit prominence learning via a prominence network in Merlin, a statistical-parametric DNN-based text-to-speech system. We build on our previous results that successfully evaluated the inclusion of an automatically extracted, speech-based prominence feature into the training and its control at synthesis time. In this work, we expand the PROMIS system by implementing the prominence network that predicts prominence values from text. We test the network predictions as well as the effects of a prominence control module based on SSML-like tags. Listening tests for the complete PROMIS system, combining a prominence feature, a prominence network and prominence control, show that it effectively controls prominence in a diagnostic set of target words. The tests also show a minor negative impact on perceived naturalness, relative to baseline, exerted by the two prominence tagging methods implemented in the control module.