关键词: 最小二乘支持向量机, 自回归滑动平均模型, 鲁棒性, 稀疏化, 模态分析

Abstract: A leastsquares support vector regression (LSSVR) technique is applied to modal parameter identification 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 evaluation and experimental validation demonstrate that the LSSVMbased method identifies structural modal parameters accurately and quickly.

Key words: least squares support vector machines (LSSVM), ARMA model, robustness, sparseness, modal analysis