本文選題：無跡卡爾曼濾波 + l范數(shù)正則化�。� 參考：《工程力學》2017年08期

【摘要】：采用傳統(tǒng)卡爾曼濾波類算法對結(jié)構(gòu)進行損傷識別時,損傷識別反問題的不適定性使得識別結(jié)果易受噪聲干擾,甚至算法不收斂。為此,該文提出了一種結(jié)合l1范數(shù)正則化的無跡卡爾曼濾波損傷識別算法。根據(jù)結(jié)構(gòu)出現(xiàn)局部損傷時其損傷參數(shù)分布具有稀疏性的特點,通過偽測量方法,將l1范數(shù)正則化引入到無跡卡爾曼濾波框架中,在改善反問題求解不適定性的同時,能有效地提高結(jié)構(gòu)局部損傷識別能力。梁、桁架結(jié)構(gòu)的數(shù)值分析與實驗研究表明,該文方法可以對損傷的位置與程度進行準確識別,且具有良好的魯棒性。
[Abstract]:When the traditional Kalman filtering algorithm is used to identify the damage of the structure, the ill-posed problem of the inverse problem of the damage identification makes the result of the identification easy to be disturbed by noise, and even the algorithm does not converge. In this paper, an unscented Kalman filter damage detection algorithm based on L 1 norm regularization is proposed. According to the sparse property of the damage parameter distribution when the local damage occurs in the structure, the L 1 norm regularization is introduced into the unscented Kalman filter framework by pseudo-measurement method, which can improve the ill-posed solution of the inverse problem at the same time. It can effectively improve the local damage identification ability of the structure. The numerical analysis and experimental study of beam and truss structures show that the proposed method can accurately identify the location and degree of damage and has good robustness.
【作者單位】： 南昌大學建筑工程學院;
【基金】：國家自然科學基金項目(51268045,51469016) 教育部高等學校博士點基金項目(20123601120011) 水沙科學與水利水電工程國家重點實驗室開放研究基金項目(sklhse-2014-C-03)
【分類號】：TU317
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本文編號：2024407