论文部分内容阅读
提出一种基于系统状态空间模型和归一化鲁棒最小均方根(NR-LMS,Normalized Robust Least Mean Square)理论的动力学结构参数辨识方法.利用系统的输入-输出数据建立其Hankel-Toeplitz模型,利用NR-LMS算法得到该模型参数的估计并求得系统的Hankel矩阵,对Hankel矩阵进行奇异值分解即可确定系统的阶次,进而确定系统状态空间模型的参数.仿真研究和实验结果表明,此方法可以准确、快速地提取出结构的参数,且抗噪能力较强.
This paper presents a method of dynamic structural parameter identification based on the system state space model and the Normalized Robust Least Mean Square (NR-LMS) theory. The Hankel-Toeplitz Model, the NR-LMS algorithm is used to get the model parameter estimation and the Hankel matrix of the system is obtained, and the singular value decomposition of the Hankel matrix can determine the order of the system and then determine the parameters of the system state-space model. Simulation and experimental results It is shown that this method can extract the structure parameters accurately and quickly, and has strong anti-noise ability.