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非线性非高斯结构系统识别的粒子滤波方法

Particle Filtering Method for Nonlinear Non-Gaussian Structural System Identification
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摘要 采用粒子滤波方法(PF方法)在非高斯噪声条件下对非线性系统进行参数识别。传统扩展卡尔曼滤波(EKF)方法具有高斯噪声假设与非线性系统线性化的缺陷,PF方法可以克服EKF方法的缺点;因此在系统识别中具有很强的鲁棒性,更适合进行非线性结构系统参数识别。数值仿真结果发现PF方法的系统识别精度高于EKF方法,证明PF方法在非线性非高斯结构系统识别中的有效性。 A particle filtering （PF）method is utilized to identify a nonlinear structural system with non- Gaussian noise. Traditional extend Kalman filtering（EKF） method has some disadvantages in Gaussian noise hypothesis and to linearize nonlinear system, but the PF method can conquer such disadvantages of EKF method. Therefore, the PF method has great robust ability. It is suitable to nonlinear non - Gaussian structural parameter estimation. The numerical simulations confirm effectiveness of the proposed method for the online structural system identification.
作者 张伟 李烨 杨晓楠
机构地区 同济大学结构工程与防灾研究所 华东建筑设计研究院有限公司
出处 《江西科学》 2008年第3期387-391,448,共6页 Jiangxi Science
基金 教育部留学回国人员科研启动基金资助项目
关键词 结构损伤识别 粒子滤波 扩展卡尔曼滤波 非线性 非高斯 Structural system identification, Particle filtering, Extend kalman filtering, Nonlinear, Non - Gaussian
分类号 TU937.2 [建筑科学—结构工程] TP274.2 [自动化与计算机技术—检测技术与自动化装置]
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参考文献8

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共引文献34

江西科学
2008年 第3期

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