• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Earthquake Engineering & Structural Dynamics >Performance of unscented Kalman filter for model updating with experimental data
【24h】

Performance of unscented Kalman filter for model updating with experimental data

机译：UNSCENTED卡尔曼滤波器的性能进行模型更新实验数据

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The unscented Kalman filter (UKF) is one of the most widely used algorithms for identifying and updating numerical model parameters. When updating from experimental data, the UKF performs well but it is sensitive to the selection of the initial algorithm variables and vulnerable to the influence of measurement noise. Furthermore, the ability to capture new behavioral features, such as hardening at large displacements, is a challenge. To achieve a more robust algorithm that can learn new features, techniques from the constrained UKF and the adaptive UKF are combined with an additional weighting on learning based on the magnitude of the input. The weighted adaptive constrained unscented Kalman filter (WACUKF) updates the numerical model with adaptive calculation of measurement noise and assigns a different learning rate for measurement data through the weighting function. To confine parameter values in the ranges with physical sense, the WACUKF adopts the sigma points projecting strategy for constrained parameters. Data from two experiments conducted on different structural components, in different laboratories, with different setups are used to validate the effectiveness of the WACUKF and compare against the performance of the UKF. The results demonstrate that the WACUKF can capture and preserve new features during model updating and is more robust and accurate than the UKF.
机译：Unscented Kalman滤波器（UKF）是用于识别和更新数字模型参数的最广泛使用的算法之一。从实验数据更新时，UKF执行良好，但对初始算法变量的选择很敏感，并且容易受到测量噪声的影响。此外，捕获新的行为特征的能力，例如在大型位移中的硬化，是一个挑战。为了实现可以学习新功能的更强大的算法，来自约束UKF的技术和自适应UKF的技术基于输入的幅度与额外加权相结合。加权自适应约束无创的卡尔曼滤波器（Wacukf）更新具有测量噪声的自适应计算的数值模型，并通过加权函数分配用于测量数据的不同学习速率。为了限制具有物理意义的范围内的参数值，Wacukf采用Sigma点投影策略进行约束参数。在不同的结构组件上进行的两个实验，在不同的实验室中进行的两个实验用于验证Wacukf的有效性并与UKF的性能进行比较。结果表明，Wacukf可以在模型更新期间捕获和保留新功能，并且比UKF更强大，更准确。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号