摘要
主要对比研究了基于环境激励的随机子空间法和Polymax算法。随机子空间法是一种时域模态识别方法,它直接将结构的响应数据构建成Hankel矩阵,并利用矩阵的LQ分解、奇异值分解和特征值分解等数学方法来识别结构的模态参数,其中在相邻模态的识别方面,它明显优于一些传统的模态识别方法。
The stochastic subspace method and Polymax based on environmental incentives were researched contrastively on this paper.Random subspace method is time-domain modal identification methods,it directly uses structural response data to build into a Hankel matrix,using matrix LQ decomposition, singular value decomposition, eigenvalue decomposition and other mathematical methods to identify structural modal parameters,especially in the context of adjacent modal identification,it should be much better than some of traditional response-based modal identification methods.
出处
《煤矿机械》
北大核心
2010年第8期106-108,共3页
Coal Mine Machinery
基金
山西省重点实验室开放基金资助项目(2008012013-6)
山西省科技攻关项目(20090322015)
关键词
随机子空间法
Polymax
环境激励
模态识别
random subspace method
polymax
environmental incentives
modal identification
