NON-LINEAR HYSTERETIC STRUCTURAL IDENTIFICATION BY UTILIZING ON-LINE SUPPORT VECTOR REGRESSION
NON-LINEAR HYSTERETIC STRUCTURAL IDENTIFICATION BY UTILIZING ON-LINE SUPPORT VECTOR REGRESSION
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DOI：
10.2208/jsceseee.23.45s
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发表时间：
2006-07
期刊：
影响因子：
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通讯作者：
Jian Zhang;Tadanobu Sato
中科院分区：
文献类型：
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作者：
Jian Zhang;Tadanobu Sato
结构在强地震等严酷载荷下表现出高度非线性特征。因此，土木工程中非线性结构识别至关重要。然而，由于结构模型的复杂性以及输入和输出（I/O）数据中存在的强噪声，非线性滞后结构识别仍然是一个具有挑战性的课题。本文提出了一种基于增量支持向量回归（SVR）的有效方法来在线识别非线性迟滞结构参数。 SVR 中采用了一种新颖的不敏感损失函数，而不是最小二乘法中使用的高斯损失函数，因此建议的基于 SVR 的方法可以产生稳健且准确的识别结果。此外，作为一种用于以顺序方式训练SVR的增量算法，所提出的基于SVR的方法不仅工作速度快，而且可以在线识别非线性结构本构参数。该方法的性能通过五自由度非线性滞回结构识别问题进行了验证，其中两种情况（功率参数已知/未知）均被研究。识别结果显然表明，即使测量数据存在噪声，所提出的技术在非线性结构识别的鲁棒性和准确性方面也具有潜在的性能。
Structures exhibit highly nonlinear characters under severe loads such as strong seismic excitations. Therefore, it is crucial to make nonlinear structural identification in civil engineering. However, nonlinear hysteretic structural identification is still a challenging topic due to structural model complexity and the strong noises existing in input and output (I/O) data. An efficient approach based on the incremental support vector regression (SVR) is proposed here to identify nonlinear hysteretic structural parameters on-line. Instead of the Gaussian loss function utilized in the least squares method, a novel einsensitive loss function is employed in SVR, and therefore the suggested SVR-based approach produces robust and accurate identification results. Furthermore, as an incremental algorithm employed to train SVR in a sequential way, the presented SVR-based approach not only works rapidly, but also identifys nonlinear structural constitutive parameters on-line. The performance of the proposed approach is verified by a five degree of freedom nonlinear hysteretic structural identification problem, in which two cases (power parameter is known/unknown) are both investigated. The identified results show evidently that the proposed technique has potential performance in robustness and accuracy for nonlinear structural identification, even when the measurement data in the presence of noises.