基于QPSO-DBN集成学习的城轨列车定位研究  

Research on the positioning of urban rail train basedon QPSO-DBN ensemble learning

作　　者：徐凯[1] 杨锐 Xu Kai;Yang Rui(School of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学信息科学与工程学院,重庆400074

出　　处：《电子测量与仪器学报》2022年第9期21-28,共8页Journal of Electronic Measurement and Instrumentation

基　　金：四川省科技厅川渝合作重点研发项目(20ZDYF3618);重庆市自然科学基金项目(cstc2021jcyj-msxmX0017);重庆市教委科学技术研究项目(KJQN202000703)资助。

摘　　要：高精度的定位是实现列车自动驾驶的重要前提。针对现有机器学习用于列车定位时,存在特征选取理论依据不足、难以确定恰当的模型结构,从而导致列车定位数据不稳定、不精确等问题,提出了一种基于集成深度置信网络(deep belief network, DBN)的城轨列车定位新方法。该方法首先对原始数据集进行预处理,其次利用皮尔逊系数对特征进行筛选,然后基于Stacking集成模型,利用量子粒子群算法(quantum particle swarm optimization, QPSO)优化集成模型中DBN基学习器的结构。将所提出的QPSO-DBN集成模型与经典机器学习方法、传统算法优化的集成模型相比,进一步提高了列车的定位精度。最后,通过仿真实验验证了所提出模型的优越性。Highly accurate positioning is an important prerequisite for automatic train driving. In terms of the problems that the existing machine learning is used for train positioning, such as the insufficient theoretical basis for feature selection and difficulty in determining the proper structure of model, which lead to the unstable and inaccurate data about train positioning.A new positioning method about urban rail train is proposed based on an ensemble deep belief network(DBN). This method firstly preprocesses the original dataset, then uses the Pearson coefficient to filter the features, finally utilizes the quantum particle swarm algorithm(QPSO) to optimize the structure of the DBN-based learner. Comparing the proposed QPSO-DBN model with the ensemble model about the classical machine learning methods and the traditional optimized algorithms, respectively, the positioning accuracy of the train is further improved. Finally, the superiority of the proposed model is verified by simulation experiments.

关 键 词：深度置信网络 量子粒子群算法 集成学习 列车定位 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TN966[自动化与计算机技术—计算机科学与技术]