The research of model updating of using support vector machine based on hybrid basis functions

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (15) : 194-202.

CHEN Zhe，HE Huan，CHEN Guo-ping

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Abstract

In this paper, the model updating method based on surrogate model is presented. A support vector machine (SVM) based on hybrid basis function is proposed for solving the over fitting results while SVM is used to deal with weak nonlinear function. According to the dynamic experimental measured results and finite element calculated results, suitable modified parameters and modified ranges were chosen based on design requirements, sensitivity analysis results or engineering experiences, and the suitable design of experiment method (DOE) was used to choose sample points based on modified parameters and modified ranges. Then, the surrogate model for each group of sample points and corresponding objective function using SVM based on hybrid basis functions was constructed, and the multi-objective optimization algorithm is introduced to obtain the global optimal solution. It appears that surrogate model using SVM based on hybrid basis functions does a higher precision than normal SVM, and it can be used in practical application.

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Surrogate model / Multi-objective optimization / Hybrid basis function / Model updating

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CHEN Zhe，HE Huan，CHEN Guo-ping. The research of model updating of using support vector machine based on hybrid basis functions[J]. Journal of Vibration and Shock, 2017, 36(15): 194-202

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