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首页> 外文期刊>Journal of aerospace engineering >Integrating Mach Number Prediction with Outlier Detection for Wind Tunnel Systems
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Integrating Mach Number Prediction with Outlier Detection for Wind Tunnel Systems

机译：将Mach数预测与风洞系统的异常检测集成

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Mach number prediction plays a crucial role in wind tunnel systems. Due to the complicated system behavior, many existing predictors cannot obtain the desired level of accuracy. In addition, the presence of outliers in databases further negatively influences predictive accuracy. In this paper, we address these two problems in one scheme. In contrast to robust regression models, in this paper the problems of prediction and outlier detection are considered separately but are solved by one paradigm. We propose an ensemble model as a predictor, in which a Gaussian process model is used as the base learner. The motivation for using the Gaussian process is its superiority in solving complex nonlinear regression problems. The objective of the ensemble model is to further improve the predictive accuracy of the Gaussian process model. Our outlier detection model is also based on a Gaussian process. It is composed of two complementary components; one is based on Gaussian process regression, and the other is based on Gaussian process classification. We verify our predictor and outlier detection model with three data sets from a real-world wind tunnel system. The results not only verify the model's predictive performance but also underline the superiority of the detection model.
机译：马赫数预测在风洞系统中起着至关重要的作用。由于系统行为复杂，许多现有预测因子不能获得所需的精度水平。此外，数据库中的异常值的存在进一步负面影响预测性准确性。在本文中，我们在一个方案中解决了这两个问题。与强大的回归模型相比，在本文中，预测和异常检测问题分别考虑但是由一个范例解决。我们将一个集合模型提出作为预测器，其中高斯过程模型用作基础学习者。使用高斯过程的动机是解决复杂非线性回归问题的优势。集合模型的目的是进一步提高高斯过程模型的预测准确性。我们的异常检测模型也基于高斯过程。它由两个互补成分组成;一个是基于高斯进程回归，另一个是基于高斯过程分类。我们验证了我们的预测器和异常值检测模型，具有来自现实世界风洞系统的三个数据集。结果不仅验证了模型的预测性能，还验证了检测模型的优越性。

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