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Non-linear system identification of the versatile-typed structures by a novel signal processing technique

机译:新型信号处理技术对通用型结构的非线性系统识别

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摘要

Non-linear structural identification problems have raised considerable research efforts since decades, in which the Bouc-Wen model is generally utilized to simulate non-linear structural constitutive characteristic. Support vector regression (SVR), a promising data processing method, is studied for versatile-typed structural identification. First, a model selection strategy is utilized to determine the unknown power parameter of the Bouc-Wen model. Meanwhile, optimum SVR parameters are selected automatically, instead of tuning manually. Consequently, the non-linear structural equation is rewritten in linear form, and is solved by the SVR technique. A five-floor versatile-type structure is studied to show the effectiveness of the proposed method, in which both power parameter known and unknown cases are investigated.
机译:几十年来,非线性结构识别问题已经引起了相当大的研究努力,其中通常将Bouc-Wen模型用于模拟非线性结构本构特征。支持向量回归(SVR),一种有前途的数据处理方法,被研究用于通用类型的结构识别。首先,利用模型选择策略来确定Bouc-Wen模型的未知功率参数。同时,自动选择最佳SVR参数,而不是手动调整。因此,非线性结构方程式以线性形式重写,并通过SVR技术求解。研究了五层多功能型结构,以显示该方法的有效性,该方法在功率参数已知和未知情况下均进行了研究。

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