| 赵昕,$2.一类加权全局迭代参数卡尔曼滤波算法[J].计算力学学报,2002,19(4):403~408 |
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| 一类加权全局迭代参数卡尔曼滤波算法 |
| A weighted global iteration Parametric Kalman Filter Algorithm |
| 修订日期：2001-03-12 |
| DOI：10.7511/jslx20024086 |
| 中文关键词: 系统识别,参数卡尔曼滤波,加权全局迭代,非线性系统 |
| 英文关键词:system identification,Parametric Kalman Filter,weighted global iteration,nonlinear system |
| 基金项目:国家杰出青年科学基金资助项目 (5 982 5 10 5 ) |
| 赵昕 $2 |
同济大学建筑工程系 上海200092
(赵昕)
，同济大学建筑工程系 上海200092(李杰)
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| 摘要点击次数: 1307 |
| 全文下载次数: 8 |
| 中文摘要: |
| 结合参数卡尔曼滤波算法和全局迭代推广卡尔曼滤波算法本文提出了加权全局迭代参数卡尔曼滤波算法。参数卡尔曼滤波算法可避免系统参数和状态变量之间的非线性耦合 ,同时通过带有目标函数的全局迭代算法保证能够获取到稳定、收敛的识别结果。分别针对线性结构模型和随动强化双线性结构模型进行了仿真参数识别。结果显示 ,不加权的全局迭代参数卡尔曼滤波算法对线性系统是有效的 ,而对非线性系统必须使用加权的全局迭代参数卡尔曼滤波算法。当信噪比较大 ,迭代无法得到收敛的结果时 ,目标函数保证了较好识别结果的获得 |
| 英文摘要: |
| By combining Parametric Kalman Filter Algorithm with weighted global iteration procedure, a weighted global iteration parametric Kalman Filter Algorithm(PKF\|WGI)is proposed . PKF algorithm can avoid the nonlinear coupling phenomenon between system parameters and state variables, and WGI procedure with an objective function is applied to obtain the stable and convergent solutions. The identification problems are investigated for single degree of freedom linear system and bilinear hysteretic systems. Ac cording to the numerical results, PKF\|WGI of weight 1 (i.e.: WGI without weight) is effective for the identification of linear system. While, An appropriate weight should be chosen to obtain good results for the identification of nonlinear system. When noise level is high, WGI with an objective function can ensure stable and convergent results. |
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