Modelling of automotive engine dynamics using diagonal recurrent neural network

The spark-ignition (SI) engine dynamics is described as a severely nonlinear and fast process. A black-box model obtained by system identification approach is often valuable for the control and fault diagnosis application on such systems. Recurrent neural network (RNN) might be better suited for such dynamical system modelling due to its feedback back scheme if compared with feed-forward neural network. However, the computational load for RNN limits its practical application. In this paper, a diagonal recurrent neural network (DRNN) is investigated to model SI engine dynamics to achieve a balance between the modelling performance and computational burden. The data collection procedure and algorithms for training DRNN are presented too. Satisfactory results on modelling have been obtained with moderate cost on computation.