@inproceedings{b6623948396c4a7b958a691fe359744d,
title = "A Neural Network Based Soft Sensor For Air Fuel Ratio Dynamics In SI Engines",
abstract = "Soft sensors have been widely used in control algorithms of engineering application to enhance the control performance and system robustness. This paper proposes a neural network (NN) based soft sensor scheme for air/fuel ratio sensor in spark-ignition engines. The modeling results show that satisfactory modeling performance can be obtained with moderate computational load.",
keywords = "Engine dynamics, Neural network, Soft sensor, System identification",
author = "Yujia Zhai and Man, {Ka Lok} and Sanghyuk Lee and Fei Xue",
note = "Funding Information: This work was supported in part by the Centre for Smart Grid and Information Convergence (CeSGIC) at Xian Jiaotong-Liverpool University. This research was financially supported by the Centre for Smart Grid and Information Convergence (CeSGIC) at Xian Jiaotong-Liverpool University. The authors would like to thank all the parties concerned.; 2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 ; Conference date: 15-03-2017 Through 17-03-2017",
year = "2017",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "719--722",
editor = "Korsunsky, {A. M.} and Ao, {S. I.} and Oscar Castillo and Craig Douglas and Feng, {David Dagan}",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017",
}