| 城际铁路列控系统车-地通信延迟时间估计的深度学习算法研究 |
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| 引用本文： | 夏明,蒋仁钢.城际铁路列控系统车-地通信延迟时间估计的深度学习算法研究[J].铁路计算机应用,2018,27(1):55-58. |
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| 作者姓名： | 夏明 蒋仁钢 |
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| 作者单位： | 卡斯柯信号有限公司 北京分公司,北京 100055 |
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| 摘 要： | 为了降低通信延迟对城际铁路列车运行控制的影响,利用通信控制服务器（CCS）积累的车-地GSM-R消息延迟时间历史数据,形成延迟时间估计的算法框架。采用递归神经网络,结合无线传输数据包大小与延迟时间的关系,并使用规范化等深度学习技术,对数据和模型进行学习和训练。实验结果表明,可以有效地估计无线传输延迟时间,解决统计分析方法带来精确度不高的问题,为车地通信消息有效性的精确判断提供依据。
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| 关 键 词： | 城际铁路 GSM-R 延迟时间 深度学习 递归神经网络 |
| 收稿时间： | 2017-07-31 |
Prediction on communication delay time of train-ground in train control system of intercity railway based on deep learning algorithm |
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| Affiliation: | Beijing Branch, CASCO Signal Ltd., Beijing 100055, China |
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| Abstract: | To reduce the impact of communication delay on intercity railway, this paper utilized the historical data of communication delay time of train-ground GSM-R accumulated by communication control server(CCS) to form the delay time estimation algorithm framework, used recurrent neural network and standardized deep learning technology, combined with wireless transmission packet size and delay time, to learn and training the data and model. Experimental results showed that this method could effectively predict the delay time of wireless transmission, and solve the problem of low accuracy caused by statistical analysis, provide the basis for accurately judging the effectiveness of train-ground communication message. |
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