基于多最小二乘支持向量机的桥梁温度挠度效应的分离

杨 红;孙 卓;刘夏平;朱卫安;王燕萍

振动与冲击 ›› 2014, Vol. 33 ›› Issue (1) : 71-76.

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振动与冲击 ›› 2014, Vol. 33 ›› Issue (1) : 71-76.
论文

基于多最小二乘支持向量机的桥梁温度挠度效应的分离

  • 杨 红1, 孙 卓2, 刘夏平2, 朱卫安1, 王燕萍1
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SEPARATION OF BRIDGE TEMPERATURE DEFLECTION EFFECT BASED ON M-LS-SVM

  • YANG Hong1, SUN zhuo 2, LIU Xia-ping2, Zhu Wei-an 1, WANG Yan-ping 1
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摘要

根据桥梁挠度的各成分的特性,建立温度和温度挠度效应的非线性关系。为了提高温度挠度效应的拟合能力,提出多最小二乘支持向量机(M-LS-SVM)拟合模型。通过减聚类方法将输入空间划分为一些小的局部空间,在每个局部空间中用LS-SVM建立子模型。为解决子模型相互之间的严重相关问题,提高模型的精度和鲁棒性,各个子模型的预测输出通过主元递归(PCR)方法连接。实验和分析结果表明:该方法能分离挠度监测信号中的温度效应,为从长期监测信号中进行损伤识别提供基础数据。

Abstract

According to the various components characteristics of the bridge deflection, the non-linear relationship of temperature and the temperature deflection effect was established. In order to improve the regressive ability to fit the temperature deflection effect, a multiple least square support vector machine (M-LS-SVM) regressive model was presented. The subtractive clustering was adopted to divide the input space into several sub-spaces, and sub-models were built by LS-SVM in every sub-space. In order to minimize the severe correlation among sub-models and to improve the accuracy and robustness of the model, the sub-models were combined by principal components regression (PCR). Experimental and analytical results show that the method can separate the temperature effect from deflection monitoring signals and provide the basis data for damage detection from the long-term monitoring signals.



关键词

多最小二乘支持向量机 / 温度 / 温度挠度效应 / 分离

Key words

multiple least square support vector machines / temperature / deflection temperature effect / separation

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杨 红;孙 卓;刘夏平;朱卫安;王燕萍. 基于多最小二乘支持向量机的桥梁温度挠度效应的分离[J]. 振动与冲击, 2014, 33(1): 71-76
YANG Hong;SUN zhuo;LIU Xia-ping;Zhu Wei-an;WANG Yan-ping . SEPARATION OF BRIDGE TEMPERATURE DEFLECTION EFFECT BASED ON M-LS-SVM[J]. Journal of Vibration and Shock, 2014, 33(1): 71-76

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