基于神经网络的卡尔曼滤波在变形监测中的应用

Application of Kalman Filter Based on Neural Network for Building Deformation Monitoring

卡尔曼滤波是基于最小均方误差下递推式的最优估计方法,现已成为最常用的滤波算法之一。一般来说,标准的卡尔曼滤波要求动态噪声和观测噪声具有先验已知性,因此这也限制了它在实际生产中的应用,而神经网络对实际系统辨识具有很好的非线性映射能力,利用神经网络对卡尔曼滤波后的估计值进行补偿,可以在很大程度上改进卡尔曼滤波的效果,将此方法应用于变形监测中证明了该方法的可行性及有效性。

Kalman filter is an optimization estimation method of recurrence formula based on least mean square error,it has been one of the most frequently-used filter algorithms. Generally,the standard Kalman filter demands dynamic noise and observation noise with the known prior information,because of these reasons,which restricts its application in reality. However,the reality system identification by use of nonlinear mapping ability of neural network to compensate estimate error brought by imprecise system model,which is a optimal filtering algorithm,it solves the problems of Kalman filter on bad stability and low accuracy and improves the effect of Kalman filter. In this paper,the experiment result shows the provided method is effective and available in deformation monitoring application.