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(eng): In the real world, it is often possible to model certain variables using several different regressive models. However, as the theoretical (mathematical, physical...) description of the real world is almost never perfect, there exists uncertainty about the true or best-fitting model. This paper deals with an issue of `mixing' information from multiple potentially true models, which run in parallel, to obtain a single outcome, taking the model uncertainty into account. While this issue has been addressed by many research papers in the past, most of them were developed for the static cases or for the state-space models. Here, we discuss an enhancement for a class of input-output regressive models. The described method allows to switch among models to reflect their modelling performance. If there is a single best model, the method quickly converges to it.