Surrogate modeling approximation using a mixture of experts based on EM joint estimation

被引：28

作者：

Bettebghor, Dimitri ^{[1
,2
]}

Bartoli, Nathalie ^{[1
]}

Grihon, Stephane ^{[2
]}

Morlier, Joseph ^{[3
]}

Samuelides, Manuel ^{[1
,3
]}

机构：

[1] ONERA DTIM, Toulouse, France

[2] Airbus France EDSAZBT, Toulouse, France

[3] ISAE SupAero, Toulouse, France

来源：

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION | 2011年 / 43卷 / 02期

关键词：

EM clustering; Gaussian mixture models; Mixture of experts; Surrogate models; CROSS-VALIDATION; REGRESSION; SIMULATION; ENSEMBLE; SUPPORT;

D O I：

10.1007/s00158-010-0554-2

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation-Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.

引用

页码：243 / 259

页数：17

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