【摘要】 提出一种非平稳动力系统突变参数识别的序贯辅助粒子滤波方法(SAPF方法).该方法采用的重要抽样密度函数是一种依靠系统过去状态与系统最近观测量的联合密度函数,因此该方法具有较强的时域在线识别能力,比传统的粒子滤波方法更适合进行非平稳动力系统的参数识别.数值仿真结果证明了此方法在结构损伤在线识别中的有效性.更多还原

【Abstract】 A sequential auxiliary particle filtering(SAPF) method is proposed to identify a non-stationary dynamic system with abrupt changes of system parameters.In the APF,the sampling importance density is proposed as a mixture density that depends upon the past state and the most recent observations,and hence the method has a good time tracking ability.The APF,therefore is more suitable for tracking the non-stationary system than the conventional particle filtering.The numerical simulations confirm the effectiveness of the proposed method for the online structural damage identification.更多还原