摘要
监测数据深度学习预测模型运算量大、实时性差,为此结合宽度学习系统(BLS)和双向长短时记忆(Bi-LSTM)模型的优势,提出基于双向门控式宽度学习系统(Bi-G-BLS)的结构变形预测模型.对BLS的特征节点增加循环反馈和遗忘门结构,提高当前节点对前一节点的依赖关系,分别从正向和反向提取时间序列的内部特征,充分挖掘数据的双向特征,在提高模型预测精确度的同时减少模型预测时间.基于实测的地铁基坑沉降监测数据的测试结果显示,所提预测模型与门控循环单元(GRU)、BLS、Bi-LSTM、G-BLS模型相比,均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)平均分别降低了21.04%、12.81%、24.41%;在预测精度相近的情况下,所提模型的预测时间比Bi-LSTM模型降低了99.59%.结果表明,所提模型在预测速度和精确度上较对比模型有明显提升.
Aiming at the shortcomings of large computation load and poor real-time performance for deep learning models with monitoring data,combining the advantages of board learning system(BLS)and bi-directional long short term memory(Bi-LSTM)model,a structural deformation prediction model was proposed based on bi-directional gate board learning system(Bi-G-BLS).The cyclic feedback structure and the forget-gate structure were added to the feature nodes of BLS to improve the dependence of the current node on the previous node,and the internal features of the time series were extracted from the forward and reverse respectively to make full use of the bidirectional data characteristics.As a result,the prediction accuracy was improved effectively while the prediction time was greatly reduced.The test results of the measured monitoring data for the subway foundation pit settlement showed that compared with the gated recurrent unit(GRU),BLS,Bi-LSTM,and G-BLS models,the root mean square error(RMSE),mean absolute error(MAE)and mean absolute percentage error(MAPE)of the proposed model were reduced by 21.04%,12.81%and 24.41%.The prediction time of the proposed model was reduced by 99.59%compared to Bi-LSTM model with similar prediction accuracy.The results showed that the prediction speed and accuracy of the proposed model were significantly improved over the comparison models.
作者
罗向龙
王亚飞
王彦博
王立新
LUO Xianglong;WANG Yafei;WANG Yanbo;WANG Lixin(School of Information Engineering,Chang’an University,Xi’an 710064,China;Zhongxing Telecom Equipment Corporation,Xi’an 710111,China;China Railway First Survey and Design Institute Group Limited Company,Xi’an 710043,China;School of Civil Engineering and Architecture,Xi’an University of Technology,Xi’an 710048,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第4期729-736,共8页
Journal of Zhejiang University：Engineering Science
基金
国家重点研发计划资助项目(2018YFC0808706)
国家自然科学基金资助项目(52078421)
陕西省“特支计划”青年拔尖人才(陕组通字[2018]33号)
中铁一院科研项目(院科19-40)。
