Open AccessThe research on train resistance prediction of low vacuum pipeline based on different neural network algorithms
Author(s) -
Chengxin Du,
Zhifei Wang,
Liping Fan,
Ruilong Feng
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/892/1/012053
Subject(s) - artificial neural network , pipeline (software) , train , probabilistic neural network , computer science , algorithm , radial basis function , artificial intelligence , machine learning , time delay neural network , cartography , programming language , geography
In order to obtain the maximum aerodynamic resistance of low-vacuum pipeline train under different working conditions, this paper uses different neural network algorithms to predict the maximum aerodynamic resistance. First, it calculates 85 groups of maximum resistance values of trains under different operation speeds, pipeline pressure and blocking ratios. Then, it takes 81 groups of data as training samples, establishing and training RBF neural network models, which are based on three different functions, and a linear neural network model as a comparison. Finally, it verifies those models with four groups of randomly selected verification data. The results show that the RBF network prediction model based on Newrbe function has the best prediction effect. It is superior to the other two RBF models in prediction accuracy. The prediction error of the linear neural network model for the maximum resistance of the train is large, and the prediction accuracy is far lower than that of the radial basis function neural network model.