The Oil Film Parameters Identification Study of Gasoline Engine during Transient Conditions Based on Chaos-RBF Neural Network

As it is difficult to accurately determine the transient operating conditions of oil film parameter, put forward a oil film parameter distinguish method in the gasoline engine transient conditions based on Chaos-RBF. 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, While taking advantage of Chaos-RBF neural network training algorithm, the objective function to take a global minimum or close to the global minimum, and effectively improve the recognition accuracy of the model identification, and the recognition ability with BP neural network model and least square method are compared and analyzed. It shows the chaotic RBF neural network model has stronger nonlinear identification capability, this model can improve the identification accuracy of oil film parameter dynamic effectively, and then come to the oil film parameter dynamic characteristics of the different conditions.