Research on BP Neural Network Model and Algorithm for Group Vehicle Refueling System Simulation
In order to realize the simulation of the group vehicle refueling system under random working conditions,a fuel gun flow simulation method is proposed based on the improved BP neural network.Firstly,the refueling performance test data of the group vehicle are preprocessed,and the sample training set and test set are constructed.Secondly,a group vehicle refueling system simulation model based on BP neural network is built.During the process of model construction,the influence of the number of hid-den layer neurons on the model results is analyzed,and the number of hidden layer neurons is determined.The traditional BP neural network is improved,and the LM algorithm is used to solve the model convergence problem.Finally,the prediction results of the model are analyzed and compared with the traditional model.The results show that the relative error between the simulation value of the fuel quantity based on the traditional BP neural network and the actual value is 4.97%,and the relative error of the improved BP neural network based on the LM algorithm is 0.93%,which shows the improved BP neural network has a higher prediction accuracy.This method solves the simulation problem of the refueling volume of the group vehicle refueling system,and can provide data basis for the subsequent intelligent control of the group vehicle refueling system.
random working conditionsimproved BP neural networksimulationintelligent control
万方数据
维普
