| Autor: |
Yujia ZHAI, Kejun QIAN, Sanghyuk LEE, Fei XUE, Moncef TAYAHI |
| Předmět: |
|
| Zdroj: |
International Journal of Design, Analysis & Tools for Integrated Circuits & Systems; Oct2017, Vol. 6 Issue 1, p6-11, 6p |
| Abstrakt: |
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 modeling 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 modeling performance and computational burden. The data collection procedure and algorithms for training DRNN are presented. Satisfactory results on modeling have been obtained with moderate cost on computation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
