大坝监测资料的时变Kalman预测模型

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。

A Time-varying Kalman Model for Dam Monitoring Data Prediction

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.