PROGRESS IN MODEL UPDATING FOR STRUCTURAL DYNAMICS
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摘要: 叙述了结构动力模型修正方法的一般原理及与其密切相关的模型缩聚和模态扩展方法,并且挑选其中具有代表性的文献, 介绍和比较了3种主要的修正方法, 即传统的动力模型修正方法, 包括矩阵型修正方法和参数型修正方法, 和最近兴起的基于神经网络的模型修正方法, 重点分析了这些方法的优点和不足之处, 力图能使读者对于这一研究领域的发展有一个脉络清晰的了解. 最后, 就目前研究中尚未解决的问题作了一些探讨.Abstract: This paper gives a brief introduction of the generalprinciple of model updating as well as the methods of model reduction andmodal expansion closely related to model updating. Some representativereferences are singled out and introduced with particular attentionto three kinds of primary methods of modelupdating, namely, direct method and parametric method, which are known astraditional methods, and updating method with neural networks. Emphases areput on their merits in application and corresponding disadvantages.Finally, some recent unresolved problemsare discussed.
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Key words:
- model updating /
- model reduction /
- modal expansion /
- eigensensitivity /
- neural network
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