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Date of Publication:2020-01-01

Journal:Journal of Dalian University of Technology

Volume:60

Issue:6

Page Number:599-609

ISSN No.:1000-8608

Key Words:"mixed data; kernel function; support vector regression(SVR); computer experiment"

CN No.:21-1117/N

Abstract:In the process of scientific research and engineering practice,it is very common to conduct engineering experiments and numerical simulation in which the input parameters have both qualitative and quantitative factors.To achieve effective modeling of such kind of data,a method for modeling qualitative and quantitative factors based on support vector regression(SVR)is proposed for qualitative-quantitative factors analysis in engineering experiments and numerical simulation.It quantizes the correlation between qualitative factors by using the hypersphere decomposition,and describes the correlation between qualitative factors and quantitative factors by constructing a special kernel function. A support vector regression algorithm for qualitative-quantitative factors is constructed for mixed data modeling and prediction of qualitative and quantitative data.Numerical experiments and classical engineering problems show that the proposed algorithm can provide better prediction results compared with ordinary support vector regression algorithm and qualitativequantitative factor algorithms based on Gaussian process regression.Taking bone stress analysis of implant as an example,the type of implant material is considered as qualitative factors and the structural parameters as quantitative factors.Experimental results show that the proposed algorithm can significantly improve the accuracy of bone stress prediction and provide a model basis for implant design optimization,which verifies the engineering rationality of the proposed algorithm.

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