中南大学学报（自然科学版）2017，Vol.48Issue(4)：1104-1110,7.DOI:10.11817/j.issn.1672-7207.2017.04.034
基于BP神经网络的高速动车组牵引能耗计算模型
Estimating traction energy consumption of high-speed trains based on BP neural network
摘要
Abstract
In order to predict the traction energy consumption of the high-speed trains, the back-propagation (BP) artificial neural network model and the optimized train traction calculation procedures were proposed. The input variables of the back-propagation (BP) artificial neural network model were locomotive properties, slope, target speed, plan of stop and so on. And the output variable of the back-propagation (BP) artificial neural network model was the traction energy consumption of the high-speed trains. Compared with the train traction calculation procedures, the optimized train traction calculation procedures considered the train motion equation model and changed the coefficient of the resistance formula equation. The method of orthogonal experiment was used to analyze the influence factors of traction energy consumption, about 111 groups data were calculated by the two models. The result shows that the BP artificial neural network model is more accurate than the optimized train traction calculation procedures. The error between the BP neural network model and the measured value is within 4.26%, and the error between optimized train traction calculation procedures and the measured value is about 10%.When the target speed increases, the precision of BP artificial neural network model is obviously higher than that of the optimized train traction calculation procedures. The target speed and the slope have significant influence on the traction energy consumption.关键词
动车组/牵引能耗/BP神经网络/改进牵规法/因素分析Key words
multiple units/traction energy consumption/BP neural network/optimized train traction calculation procedures/factor analysis分类
交通工程引用本文复制引用
王黛,马卫武,李立清,杨叶,向初平..基于BP神经网络的高速动车组牵引能耗计算模型[J].中南大学学报（自然科学版）,2017,48(4):1104-1110,7.基金项目
国家自然科学基金资助项目(21376274) （21376274）
铁路总公司重点课题(2012Z001-B)(Project (21376274) supported by the National Natural Science Foundation of China （2012Z001-B）
Project (2012Z001-B) supported by Key Program of China Railway) （2012Z001-B）
