基于主动学习的树状高斯过程建模与参数优化

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中图分类号：TP273.2 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.06.23

Abstract：Under the framework of treed Gaussian process（TGP）modeling，a robust parameter optimization model based on an active learning algorithm for robust parameter design problems with non-stationary responses is proposed.Firstly，bycomprehensively applying the D-optimal and Expected Improvement design strategies，anactive learningalgorithm isconstructed to improve the spatialfiling performanceand optimization performance of thedesign points. Secondly，the Bayesian hierarchical modeling approach is used to construct the model structure to estimate the non-stationary functional relationship between inputs and outputs.Finally， based on the output of the TGP model，a robust parameter optimization model is constructed based on quality loss function.The genetic algorithm（GA）is used for global optimization to obtain the optimalinput parameter setings.The simulation results show that the optimal solution obtained by the proposed method has a smaller quality lossand prediction bias.Therefore，the proposed method improves the prediction accuracy in the potential optimal solution region，reduces the uncertainty of thepredicted response，and further enhances the effectiveness of robust optimization results for non-stationary responses.

Keywords：non-stationary response；robust parameter design；treed Gaussian process （TGP） model; active learningalgorithm；quality loss

0 引言

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