Self-Organizing Monte Carlo Filtering Method for Non-linear Structural system Identification
Gong Zhiguo1,2 ZHU Lei1,2,Xie Qiang3
(1.Shanghai Research Institute of Building Sciences (Group) Co., Ltd., Shanghai, 200032 2.Shanghai Key Laboratory for Innovation Technology in Engineering Structure, Shanghai, 200032 3. College of Civil Engineering, Tongji University, Shanghai, 200092)
Abstract: A self-organizing sequential Monte Carlo filter for nonlinear state-space model is proposed. An expanded state-space model is defined by augmenting the state vector with the unknown parameters of the original state-space model. The self-organizing state-space model can also be applied to the self-tuning of the noises dispersion. A local likelihood is introduced and used to select the optimal parameter from a finite number of possible values. Examples of hysteretic system identification are shown to verify the effectiveness of the proposed method.
龚治国;朱 雷阵雨;唐和生. 非线性结构系统识别的自组织蒙特卡洛滤波方法[J]. , 2009, 28(7): 111-114.
Gong Zhiguo;ZHU Lei;Xie Qiang. Self-Organizing Monte Carlo Filtering Method for Non-linear Structural system Identification. , 2009, 28(7): 111-114.