摘要：AbstractFor offline development and calibration of On-Board Diagnosis functions of the exhaust gas aftertreatment system a model of the three-way catalytic converter is essential. The goal of the method presented here is to approximate the conversion behavior of a three way catalytic converter by a model which is suitable for application in such a simulation environment. To that purpose we followed a Black Box modeling approach using Recurrent Neural Networks (RNNs) and compared different training and model settings in a case study. In order to increase training efficiency and model accuracy we investigated transfer learning techniques to transfer knowledge from physical models to the RNN model.
