NDLI: Gasoline Engine Intake Flow Forecast Study of Chaotic RBF Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Xie Fuquan Li Yuelin Li Aifan Xu Donghui Wang Chongyang Liao Borong
Copyright Year	2015
Description	Author affiliation: Sch. of Automotive & Mech. Eng., Changsha Univ. of Sci. & Technol., Changsha, China (Xie Fuquan; Li Yuelin; Wang Chongyang; Liao Borong) \|\| GuangDong Community Polytech., GuangZhou, China (Li Aifan) \|\| Phys. Sci. & Eng. Coll., Yichun Univ., Yichun, China (Xu Donghui)
Abstract	A soft predictive model which was based on Chaos-RBF neural network was proposed for the intake air flow of gasoline engine as its multidimensional nonlinear characteristics. First of all, the engine air intake flow time series with chaotic characteristics had been proved, the phase space of the original data had also been reconstructed before using RBF neural network to train and predict. And then the result had been compared with the air inlet flow average model, RBF neural network forecasting model. 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. The simulation results showed that this model is a new method to measure the intake air flow of the engine with more accuracy and timeless, which was superior to the intake air flow average model, RBF neural network prediction model.
Sponsorship	Hunan Inst. Ind.
Starting Page	506
Ending Page	511
File Size	867061
Page Count	6
File Format	PDF
e-ISBN	9781467371438
DOI	10.1109/ICMTMA.2015.129
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 forcast Atmospheric modeling gasoline engine Neural networks Time series analysis intake flow Predictive models chaotic RBF neural network Engines Petroleum
Content Type	Text
Resource Type	Article

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