摘要
在考虑随机噪声的情况下,实现了一种基于极大似然估计的多参考点频域模态参数识别方法。该方法采用频响函数的右矩阵分式模型,通过噪声的协方差矩阵对误差向量加权,使用离散时间域中基函数改善数值求解性态。模态参数的估计过程分为两步:首先由基于最小二乘估计的polyLSCF算法获取迭代初值,然后通过Gauss-Newton方法对极大似然函数进行迭代优化,得到精度更高的模态参数识别结果。采用GARTEUR仿真算例对所给出的方法进行了验证,结果表明:在高噪声情况下,利用噪声信息的极大似然估计方法能够显著提高模态参数的识别精度,特别是阻尼的识别精度。
A frequency-domain modal parameters identification method based on maximum likelihood estimation is investigated considering stochastic noise. This method uses right matrix fraction description model of frequency response function. The noise covariance matrix is adopted as weighting function. The basis function in discrete time domain is utilized for improving numerical condition. First, the least square estimation is implemented to get the initial value of modal parameters. Then, the iterative optimization of Gauss-Newton method is carried out to get more precise identification result. A simulation case of GARTEUR model is employed to validate the method. Results show that the accuracy of modal parameters is improved obviously from maximum likelihood estimation method under high noise, especially for the damping ratio accuracy.
出处
《工程力学》
EI
CSCD
北大核心
2013年第10期65-70,共6页
Engineering Mechanics
基金
中央高校基本科研业务费专项基金项目(12CX04051A)
青岛市科技计划项目(1314204jch)
