Heterogeneous mixture-of-experts for fusion of locally valid knowledge-based submodels

Real-world applications often require the joint use of datadriven and knowledge-based models. While data-driven models are learned from available process data, knowledge-based models are able to provide additional information not contained in the data. In this contribution, we propose a method to divide the input space on the basis of the validity ranges of the knowledge-based models. By doing so they are only active in those domains they are designed for. The data-driven models complete the coverage of the input space. We demonstrate the benefits of our approach on a real-world application for the energy management of a hybrid electric vehicle.