Air Fuel Ratio Identification of Gasoline Engine during Transient Conditions Based on Elman Neural Networks

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Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1

Jinan, China

October 16-October 18

ISBN: 0-7695-2528-8

	ASCII Text	x
	Zhixiang Hou, Quntai Sen, Yihu Wu, "Air Fuel Ratio Identification of Gasoline Engine during Transient Conditions Based on Elman Neural Networks," Intelligent Systems Design and Applications, International Conference on, vol. 1, pp. 32-36, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006.

	BibTex	x
	@article{ 10.1109/ISDA.2006.86, author = {Zhixiang Hou and Quntai Sen and Yihu Wu}, title = {Air Fuel Ratio Identification of Gasoline Engine during Transient Conditions Based on Elman Neural Networks}, journal ={Intelligent Systems Design and Applications, International Conference on}, volume = {1}, year = {2006}, isbn = {0-7695-2528-8}, pages = {32-36}, doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.86}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Intelligent Systems Design and Applications, International Conference on TI - Air Fuel Ratio Identification of Gasoline Engine during Transient Conditions Based on Elman Neural Networks SN - 0-7695-2528-8 SP32 EP36 A1 - Zhixiang Hou, A1 - Quntai Sen, A1 - Yihu Wu, PY - 2006 KW - null VL - 1 JA - Intelligent Systems Design and Applications, International Conference on ER -

Zhixiang Hou, Central South University, China

Quntai Sen, Central South University, China

Yihu Wu, Changsha University of Science and Technology, China

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.86

Air fuel ratio is a key index affecting power performance and fuel economy and exhaust emissions of the gasoline engine, whose accurate model is the foundation of accuracy air fuel ratio control. Taking HL495 engine as experimental device, a method of indenting air fuel ratio based on elman neural network was provided in this paper. Experiment results show the air fuel ratio model based on elman neural network has simple structure and can accurately approximate the air fuel ratio transient process and average relative error is less than 1%. The air fuel ratio based on elman neural network is better than the air fuel ratio model based on BP neural network.

Citation:

Zhixiang Hou, Quntai Sen, Yihu Wu, "Air Fuel Ratio Identification of Gasoline Engine during Transient Conditions Based on Elman Neural Networks," isda, vol. 1, pp.32-36, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006

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