《计算机应用研究》|Application Research of Computers

一种改进的深度置信网络在交通流预测中的应用

Application of improved deep belief network in traffic flow forecasting

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作者 赵庶旭,崔方
机构 兰州交通大学 电子与信息工程学院,兰州 730070
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)03-026-0772-04
DOI 10.19734/j.issn.1001-3695.2017.10.0988
摘要 针对现有交通流预测方法忽视对交通流数据自身特征的有效利用以及不能模拟更复杂的数学运算,提出了一种改进深度置信网络(deep belief network,DBN)的交通流预测方法。该方法结合深度置信网络模型与softmax回归作为预测模型,利用连续受限玻尔兹曼机(continuous restricted Boltzmann machines,CRBM)处理输入特征向量,利用自适应学习步长(adaptive learning step,ALS)减少RBM训练网络模型时重建误差所需的时间,用改进的深度置信网络模型进行交通流特征学习,在网络顶层连接softmax回归模型进行流量预测。实验结果表明,在实际的交通流数据预测中,改进的DBN模型的预测准确率以及时间复杂度相比传统预测模型都得到了较好的改善。
关键词 交通流预测;深度置信网络;连续受限玻尔兹曼机;自适应学习步长
基金项目 甘肃省科技支撑计划基金资助项目(1504GKCA018)
本文URL http://www.arocmag.com/article/01-2019-03-026.html
英文标题 Application of improved deep belief network in traffic flow forecasting
作者英文名 Zhao Shuxu, Cui Fang
机构英文名 SchoolofElectronic&InformationEngineering,LanzhouJiaotongUniversity,Lanzhou730070,China
英文摘要 In view of the fact that the existing traffic flow prediction methods ignore the efficient use of the traffic flow data characteristics and can not simulate more complex mathematical operations, this paper proposed a traffic flow prediction method to improve the deep belief network(DBN).The proposed method combined deep belief network model and softmax regression as prediction models, and used the continuous restricted Boltzmann machine(CRBM) to process the input eigenvectors and reduced the adaptive learning step(ALS) RBM trains the time needed to reconstruct the network model, and used the improved deep belief network model to study the traffic flow characteristics.It connected softmax regression model to the top of the network for traffic prediction.The experimental results show that in the actual traffic flow data prediction, the improved prediction accuracy and time complexity of the improved DBN model are better than the traditional prediction model.
英文关键词 traffic flow forecast; deep belief network; continuous restricted Boltzmann machine; adaptive learning step
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收稿日期 2017/10/30
修回日期 2017/12/15
页码 772-775,785
中图分类号 TP181
文献标志码 A