论文标题
高斯流程具有输入位置错误和复合零件组装过程的应用程序
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process
论文作者
论文摘要
在本文中，我们研究了使用输入位置误差的高斯过程建模，其中输入被噪声损坏。在这里，根据目标是否没有观察到的位置是否存在噪声，考虑了两种情况的最佳线性无偏预测指标。我们表明，如果目标未观察到的噪声，则平均平方的预测误差会收敛到非零常数，如果目标未观察到的位置没有噪声，则提供平方平方预测误差的上限。我们研究了随机kriging在预测输入位置误差的高斯过程中的使用，并证明当样本大小较大时，随机kriging是一个很好的近似值。给出了几个数字示例来说明结果，并提供了有关复合零件组装的案例研究。附录中提供了技术证明。
In this paper, we investigate Gaussian process modeling with input location error, where the inputs are corrupted by noise. Here, the best linear unbiased predictor for two cases is considered, according to whether there is noise at the target unobserved location or not. We show that the mean squared prediction error converges to a non-zero constant if there is noise at the target unobserved location, and provide an upper bound of the mean squared prediction error if there is no noise at the target unobserved location. We investigate the use of stochastic Kriging in the prediction of Gaussian processes with input location error, and show that stochastic Kriging is a good approximation when the sample size is large. Several numeric examples are given to illustrate the results, and a case study on the assembly of composite parts is presented. Technical proofs are provided in the Appendix.
