基于高斯过程模型的多响应稳健参数设计

针对高维试验数据的稳健参数设计问题, 在高斯过程(Gaussian process, GP)的建模框架下, 采用部分平行的GP(parallel partial GP, PPGP)模型来构建试验因子与多质量特性之间的响应曲面, 在此基础上运用多元质量损失函数作为优化指标来获得可控因子的最佳参数设计值。并且以一个经典仿真算例和两个实际案例验证了所提方法的有效性和优劣性。研究结果表明，与独立建模的单变量GP模型或Kriging模型比较而言, 所提方法不仅能够有效地处理高维试验数据的建模与参数优化问题, 而且能够获得更为稳健的优化结果, 运行效率更高。

Robust parameter design of multiple responses based on Gaussian process model

To address the problem of robust parameter design for high-dimensional experimental data, the parallel partial Gaussian process (PPGP) model is adopted to construct the response surfaces between the test factors and the multivariate quality characteristics under the modeling framework of Gaussian process (GP). On this basis, the multivariate quality loss function is used as an optimization index to obtain the optimal parameter design values of the controllable factors. A classic simulation examples and two actual cases are used to verify the effectiveness and advantages of the proposed method. The research results show that compared with the independent modeling of the univariate GP model or the Kriging model, the proposed method can not only deal with the modeling and parameter optimization problems of high-dimensional experimental data effectively, but also obtain more robust optimization results and higher operational efficiency.