Gasoline Engine Intake Flow Forecast Study of Chaotic RBF Neural Network

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.