JUCS - Journal of Universal Computer Science 24(9): 1330-1342, doi: 10.3217/jucs-024-09-1330
Modelling of Automotive Engine Dynamics using Diagonal Recurrent Neural Network
Yujia Zhai‡, Kejun Qian§, Fei Xue‡, Moncef Tayahi‡‡ Xian Jiaotong-Liverpool University, Suzhou, China§ Suzhou Power Supply Company, Suzhou, China
Corresponding author: Yujia Zhai ( yujia.zhai@xjtlu.edu.cn ) © Yujia Zhai, Kejun Qian, Fei Xue, Moncef Tayahi. This article is freely available under the J.UCS Open Content License. Citation:
Zhai Y, Qian K, Xue F, Tayahi M (2018) Modelling of Automotive Engine Dynamics using Diagonal Recurrent Neural Network. JUCS - Journal of Universal Computer Science 24(9): 1330-1342. https://doi.org/10.3217/jucs-024-09-1330 |  |
Abstract
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.
Keywords
diagonal recurrent neural network, dynamical system modelling, spark-ignition engine, system identification