大型复杂钢管焊接结构损伤识别方法研究

将支持向量机引入响应面重构计算中,利用支持向量机对小样本数据优秀的拟合和泛化能力,提出了一种最小二乘支持向量机响应面新方法,并将其应用于大型钢管焊接结构的模型修正及损伤识别中。对最小二乘支持向量机响应面的核函数进行了加权,提出一种综合了一次多项式核函数的线性模拟能力和高斯核函数非线性拟合能力的线性-高斯组合核函数。同时对训练样本进行了尺度变换，并对训练样本的选择方法进行了改进。通过损伤识别数值仿真及实验验证，与传统灵敏度方法进行了对比,结果表明改进响应面方法的识别效果更好,且收敛性及精度也大大提高了,为解决大型复杂结构的损伤识别问题提供了新的思路。

Study on Damage Identification Methods of Large and Complex Steel Tube Welded Structures

According to the excellent learning ability and generalization of SVM even with small samples, combined the RSM with  LS-SVM, then a new RSM was established based on LS-SVM, and it was used in model updating and damage identification. A weight method was proposed in kernel function, and the linear-and-Gaussian kernel function was proposed which combined the linear fitting ability of the linear polynomial kernel function and the nonlinear fitting ability of the Gaussian kernel function. At the same time, the training samples had a scale transformation, and the selection method of training samples was improved. By a identification example and experimentation, compared with the traditional sensitivity method, the results show that the improved RSM is better, and the convergence is also improved greatly.