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Multi-Sensor Data Fusion Prediction Model of Air Inflow Velocity Under Transitional Working Condition of Gasoline Engine

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成果类型:
期刊论文、会议论文
作者:
Hou Zhixiang*;Wei Liang
通讯作者:
Hou Zhixiang
作者机构:
[Hou Zhixiang; Wei Liang] Changsha Univ Sci & Technol, Coll Automobile & Mech Engn, Changsha 410076, Hunan, Peoples R China.
通讯机构:
[Hou Zhixiang] C
Changsha Univ Sci & Technol, Coll Automobile & Mech Engn, Changsha 410076, Hunan, Peoples R China.
语种:
英文
关键词:
Gasoline Engine;Multi-Sensor Data Fusion;Neural Network;Air Inflow Velocity;Modeling
期刊:
Sensor Letters
ISSN:
1546-198X
年:
2011
卷:
9
期:
4
页码:
1477-1481
会议名称:
3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)
会议时间:
JAN 06-07, 2011
会议地点:
Shanghai, PEOPLES R CHINA
会议主办单位:
(1) College of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan 410076, China
会议赞助商:
IEEE Instrumentat & Measurement Soc, Shanghai Univ Engn Sci, City Univ Hong Kong, Changsha Univ Sci & Technol, Hunan Univ Sci & Technol
出版地:
26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA
出版者:
American Scientific Publishers, 25650 North Lewis Way, Stevenson Ranch, California, 91381-1439, United States
机构署名:
本校为第一且通讯机构
院系归属:
汽车与机械工程学院
摘要:
A neural network-based multi-sensor data fusion prediction model of air inflow velocity under working condition is proposed and a neural network topology of air inflow velocity prediction under transitional working condition is set in this paper to mitigate the air-fuel ratio control inaccuracy resulting from air flow sensor lag. Simulation is conducted on the basis of HQ495 engine experimental data, which shows that neural network-based multi-sensor data fusion prediction model of air inflow veloci...

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