基于LS-SVM的模态参数识别方法

 最小二乘支持向量机回归用于系统的模态参数识别研究。针对经典的最小二乘支持向量回归缺少鲁棒性和稀疏性的缺陷，提出了一种兼具鲁棒性和稀疏性的最小二乘支持向量回归的算法，并保持了它原有的计算速度快的优点。最后，结合结构动力学方程的自回归滑动平均时间序列形式，给出了结构的模态参数提取方法和流程，给出了相应的数值算例以及进行了实验的检验证明。结果表明，本文的方法能够快速、准确地提取出系统的模态参数。

LS-SVM-based Method for Modal Parameter Identification

A least-squares support vector regression (LS-SVR) technique is applied to modal parameter identifi-cation in this article. While the present least squares support vector machines (LS-SVM) exhibit two natural drawbacks of insufficient robustness and sparseness, a novel algorithm that can overcome these drawbacks is proposed. An LS-SVM-based method employing the auto regression moving average (ARMA) time series is presented for linear structural parameter identification using the observed vibration data. Both numerical evalu-ation and experimental validation demonstrate that the LS-SVM-based method identifies structural modal pa-rameters accurately and quickly.