System identification of locomotive diesel engines with autoregressive neural network

As a complex nonlinear system, modeling analysis is an important method to the research of the static and dynamic characters of the locomotive diesel engines. Also the model facing the control behavior verification can be used in the electronic control unit's HIL(hardware-in-the-loop) simulation which can lessen the cost and uncertain factors in the later platform experiments. With the mode of system identification, this paper builds the dynamic model of the diesel with neural network. Aiming at the nonlinear character of the diesel with large inertia, the paper uses NARMAX(Nonlinear Auto-Regressive Moving Average with eXogenous inputs) as the main structure and uses LM(Levenberg-Marquardt) algorithm to train the network. In order to overcome the redundancy of the network structure caused by the artificial experience, this paper puts forward the Time Cycle Comparison method to determine the ranks of the input signals and uses the Optimal Brain Surgeon strategy to optimize the network structure. The simulation results proves that this method can eliminate the redundancy and improve the generalization capability of the network commendably under the same output error scopes. Comparison between the train results and the measured results shows that the dynamic model has the good real-time performances and little output error. So the model can meet the need of the system character analysis and technology application.