摘要
本次通过基于粒子群优化算法与BP神经网络相结合的方式对高铁客运量进而预测,利用粒子群优化算法对BP神经网络进行优化与训练,通过经过改进的BP神经网络对高铁客运量进行预测。经实验研究发现,本次研究所提出的预测算法比常规BP神经网络模型预测精度更高,在样本数据量较少的情况下有明显的应用优势。
In this paper, based on the combination of particle swarm optimization algorithm and BP neural network, the passenger volume of high-speed railway is predicted, and the BP neural network is optimized and trained by using particle swarm optimization algorithm, and the passenger volume of high-speed railway is predicted through the improved BP neural network. The experimental study shows that the prediction algorithm proposed in this study has higher prediction accuracy than the conventional BP neural network model, and has obvious application advantages in the case of less sample data.
作者
崔乃丹
CUI Nai-dan(Shaanxi Railway Institute,Weinan 714000 China)
出处
《自动化技术与应用》
2022年第4期148-150,共3页
Techniques of Automation and Applications
基金
陕西铁路工程职业技术学院科学研究基金项目(KY2018-67)。
关键词
BP神经网络
粒子群优化算法
高铁客运量预测
BP neural network
particle swarm optimization algorithm
high speed railway passenger volume prediction
