期刊论文

Wu, Gang

英文

中南大学学报(英文版)

2095-2899

2020

27

9

2687-2695

10.1007/s11771-020-4491-y

Project(51176014) supported by the National Natural Science Foundation of China, Project(2016JJ2003) supported by Natural Science Foundation Project of Hunan Province, China; Project(KF1605) supported by Key Laboratory of Safety Design and Reliability Technology of Engineering Vehicle in Hunan Province, China

本校为通讯机构

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 ill...

为了确保进气道喷射燃料汽油机的空燃比的控制精度,本文使用混沌径向基函数神经网络来预测进气流量。由于进气流量时间序列被证明是多维非线性和混沌的,于是选用径向基函数神经网络用于训练初始数据的重构相空间。然后,利用混沌算法定义输出层连接权重和隐层的高斯函数径向基中心,加快网络的收敛速度。通过Matlab/Simulink软件进行仿真,结果表明该模型可以获得比单纯的径向基函数神经网络模型更高的预测精度。对于实验与仿真数据,混沌径向基函数神经网络模型预测值的平均绝对误差达到0.001715,平均相对误差达到0.48205。