关键词: 混沌RBF神经网络, 进气流量, 预测, 汽油机

Abstract: A soft predictive model based on Chaos-RBF neural network is proposed for the intake air flow of gasoline engine as its multidimensional nonlinear characteristics. The engine air intake flow time series with chaotic characteristics have been proved；the phase space of the original data has also been reconstructed before using RBF neural network to train and predict. And then, the result has been compared with the air inlet flow average model and 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 show that this model is a new method to measure the intake air flow of the engine with more accuracy and timeless, which is superior to the intake air flow average model and RBF neural network prediction model.

Key words: chaotic RBF neural network, intake flow, forcast, gasoline engine