[1]周永阳,张锐,张恒煜,等. 基于LSTSVR 的路基沉降组合预测模型[J].哈尔滨理工大学学报,2017,(06):62-66.[doi:10. 15938 /j. jhust. 2017. 06. 012]
 ZHOU Yong-yang,ZHANG ui,ZHANG Heng-yu,et al. Combined Forecasting Model of Subgrade Settlement Based on LSTSVR[J].哈尔滨理工大学学报,2017,(06):62-66.[doi:10. 15938 /j. jhust. 2017. 06. 012]
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 基于LSTSVR 的路基沉降组合预测模型()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2017年06期
页码:
62-66
栏目:
材料科学与工程
出版日期:
2017-12-25

文章信息/Info

Title:
 Combined Forecasting Model of Subgrade Settlement Based on LSTSVR
文章编号:
1007- 2683( 2017) 06- 0062- 05
作者:
周永阳 张锐 张恒煜 丁鹏
 ( 哈尔滨理工大学自动化学院,黑龙江哈尔滨150080)
Author(s):
 ZHOU Yong-yang ZHANG Rui ZHANG Heng-yu DING Peng
 ZHOU Yong-yang, ZHANG Rui, ZHANG Heng-yu, DING Peng
( School of Automation,Harbin University of Science and Technology,Harbin 150080,China)
关键词:
 路基沉降预测 组合预测 最小二乘双支持向量回归机
Keywords:
 subgrade settlement prediction combination forecast model least square twin support regression
分类号:
TU432
DOI:
10. 15938 /j. jhust. 2017. 06. 012
文献标志码:
A
摘要:
 鉴于路基沉降各种单相预测模型均有其适用范围,总体预测波动性较大,精度较低,提
出基于最小二乘双支持向量回归机( LSTSVR,least square twin support vector regression) 的路基沉降
组合预测模型。该模型的核心是根据路基沉降的发展规律及其沉降曲线的特点,选择具有S 型特
点的成长曲线特征的单相预测模型; 以各单项预测模型预测结果作为最小二乘双支持向量回归机
的输入向量,构建路基沉降组合预测模型。对比试验表明: 提出方法具有更好的预测精度和稳
定性
Abstract:
 Due to the normal forecasting methods for subgrade settlement using observation data have different
applications,and the predicting results has bigger volatility and lower accuracy. The Combined forecasting model of
subgrade settlement based on Least Square Twin Support Vector Regression ( LSTSVR) is proposed in this paper. Its
core is that the growth curves with the S-type characteristics are treated as single forecasting model according to the
basic settlement law of subgrade and characteristics of settlement curve. Considering prediction results of each
individual model as the least square support vector regression model input and the combined forecasting model of
subgrade settlement is constructed. The result of engineering practice shows that the proposed method has better
prediction accuracy and stability

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更新日期/Last Update: 2018-03-10