大坝监测资料的时变Kalman预测模型

李子阳; 郭丽; 顾冲时

大坝监测资料的时变Kalman预测模型

¹南京水利科学研究院,南京市广州路223号210029;²南京体育学院附校部,南京市灵谷寺路8号210014;³河海大学水文水资源与水利工程科学国家重点实验室,南京市西康路1号210098;⁴河海大学水资源高效利用与工程安全国家工程研究中心,南京市西康路1号,210098

基金项目: 国家科技支撑计划资助项目(2006BAC14B03,2008BAB29B06)；国家自然科学基金资助项目(50809025)；中国水电工程顾问集团公司科技资助项目(CHC-KJ-2007-02)

详细信息

作者简介:
李子阳,博士生,主要从事大坝安全监控研究。

中图分类号: P228.41
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出版历程
- 收稿日期: 2010-06-18
- 修回日期: 2010-06-18
- 发布日期: 2010-08-04

A Time-varying Kalman Model for Dam Monitoring Data Prediction

¹Nanjiang Hydraulic Research Institute,233 Guangzhou Road,Nanjing 210029,China;(2 Nanjing Sport Institute,8 Linggusi Road,Nanjing 210014,China;(3 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,1 Xikang Road,Nanjing 210098,China;(4 National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety,Hohai University,1 Xikang Road,Nanjing 210098,China

Funds: 国家科技支撑计划资助项目(2006BAC14B03,2008BAB29B06)；国家自然科学基金资助项目(50809025)；中国水电工程顾问集团公司科技资助项目(CHC-KJ-2007-02)

摘要

摘要: 基于对大坝监测资料预测模型时变性的要求,在模型LS参数求解过程中引入遗忘因子,提出了能够实现模型参数实时更新的IWRLS算法。在此基础上,为使预测模型体现物理含义的同时实现滤波操作,在Kalman滤波方程组中融入统计模型、ARMA等多种方法,由此建立了考虑白色观测噪声的时变Kalman预测模型。实例分析表明,时变Kalman模型拟合及预测精度均优于传统统计模型,为大坝监测资料的预测分析提供了新思路。
- 监测资料 /
- 时变Kalman滤波 /
- 预测
Abstract: Based on time-varying requirements of prediction model for dam monitoring data,the forgetting factor is introduced to set up a forgotten matrix to give prominence to the contributions of recent data.Then the IWRLS algorithm is made to achieve updating model parameters at real-time.On this basis,in order to reflect the physical meaning and complete the filtering operation at the same time,a statistical model and ARMA are introduced into the Kalman filter equations.In the equations,state equation is established by self-variable which reflects the state characteristics with ARMA,and observation equation is established by dependent variable which reflects physical meaning with statistical models.So considering the white noise,the time-varying Kalman prediction model is established with the comprehensive functions.Case analysis shows that the fitting and forecast accuracy of time-varying Kalman model are superior to those traditional statistical models.
- monitoring data /
- time-varying Kalman filter /
- prediction

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文章访问数: 740
HTML全文浏览量: 42
PDF下载量: 356
被引次数: 0

大坝监测资料的时变Kalman预测模型

作者简介: 李子阳,博士生,主要从事大坝安全监控研究。

计量

出版历程

A Time-varying Kalman Model for Dam Monitoring Data Prediction

计量

出版历程

目录

作者简介:
李子阳,博士生,主要从事大坝安全监控研究。