NDLI: The Oil Film Parameters Identification Study of Gasoline Engine during Transient Conditions Based on Chaos-RBF Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Xie Fuquan Li Yue Lin Li Aifan Xu Donghui Gong Hongyi Liao Borong
Copyright Year	2015
Description	Author affiliation: Sch. of Automotive & Mech. Eng., Changsha Univ. of Sci. & Technol., Changsha, China (Xie Fuquan; Li Yue Lin; Gong Hongyi; Liao Borong) \|\| GuangDong Community Polytech., Guangzhou, China (Li Aifan) \|\| Phys. Sci. & Eng. Coll., Yichun Univ., Yichun, China (Xu Donghui)
Abstract	As it is difficult to accurately determine the transient operating conditions of oil film parameter, put forward a oil film parameter distinguish method in the gasoline engine transient conditions based on Chaos-RBF. Chaos algorithm is used to determine and optimal the implied Gaussian radial basis function center and the out put layer connection weights, in order to accelerate the convergence rate of RBF neural network, While taking advantage of Chaos-RBF neural network training algorithm, the objective function to take a global minimum or close to the global minimum, and effectively improve the recognition accuracy of the model identification, and the recognition ability with BP neural network model and least square method are compared and analyzed. It shows the chaotic RBF neural network model has stronger nonlinear identification capability, this model can improve the identification accuracy of oil film parameter dynamic effectively, and then come to the oil film parameter dynamic characteristics of the different conditions.
Sponsorship	Hunan Inst. Ind.
Starting Page	1228
Ending Page	1233
File Size	815847
Page Count	6
File Format	PDF
e-ISBN	9781467371438
DOI	10.1109/ICMTMA.2015.299
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Oil Film Parameter Films Neural networks Transient Conditions Identification Calibration Mathematical model Fuels Chaos-RBF Neural Network Engines
Content Type	Text
Resource Type	Article

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