自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 27    No. 9    September 2020

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Intelligent prediction on air intake flow of spark ignition engine by a chaos radial basis function neural network
LI Yue-lin(李岳林)1, 2, LIU Bo-fu(刘博夫)1, WU Gang(吴钢)1, 2, LIU Zhi-qiang(刘志强)1, 2,DING Jing-feng(丁景峰)1, 2, ABUBAKAR Shitu3

1. Key Laboratory of Safety and Design and Reliability Technology of Engineering Vehicles in Hunan Province, Changsha 410114, China;
2. College of Automotive and Mechanical Engineering, Changsha University of Science and Technology,Changsha 410114, China;
3. College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China

Abstract:To ensure the control of the precision of air-fuel ratio (AFR) of port fuel injection (PFI) spark ignition (SI) engines, a chaos radial basis function (RBF) neural network is used to predict the air intake flow of the engine. The data of air intake flow is proved to be multidimensionally nonlinear and chaotic. The RBF neural network is used to train the reconstructed phase space of the data. The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer. The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model. The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48, respectively.

 

Key words: intake air flow; spark ignition engine; chaos; RBF neural network

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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