Weighting influence of two neural networks on classification
As neural networks are non-parametric, it would usually be the case that you train the combined model (as you describe it, but perhaps with additional FC-layers at the end) and let the model as a single entity learn/decide how best to combine the output of your two sub-models.

If you want to the hard-coded percentages you mention, then the most straight-forward way would be to do what you mention in the last paragraph. 

People often train the same (or slightly differing models) independently on the same dataset, then average the predictions of these models on the test set, which can improve overall performance by integer percentages.