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Sequential ∈-Support Vector Regression based Online Robust Parameter Design
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DOI：10.1016/j.cie.2021.107391.png)
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Abstract
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The existing online Robust Parameter Design (RPD) focuses on updating the optimal setting from the perspective of Bayesian and adopting the low-order polynomial model as the response surface. Polynomial model, especially the low-order, has poor capability for fitting data, particularly for nonlinear data. Adopting an inaccurate model as a response surface will result in obtaining an unreliable recommended optimal setting. In this paper, a machine-learning method to RPD is proposed to recompute the optimal control factor settings based on the optimal control factor settings in the former stage and the online measurements of the noise factors in the current stage. A single response model approach to RPD is adopted. In the proposed method, the response surface is constructed using the sequential is an element of-Support Vector Regression (SVR) model, which combines the control factor settings and the online measurements of the noise factors as the input factors and regard the specified quality characteristic as the output factor. When the new measurement of the noise is available, the online SVR can be updated based on the current control factor settings and the observed quality characteristic; and then the RPD process can be redone according to the updated response surface (constructed by SVR model); and eventually the updated control factor settings can be obtained. The proposed methodology is confirmed using three examples, which illustrate that the underlying optimal settings can be found by utilizing the proposed online RPD strategy.
Keywords:
Support Vector Regression
Robust Parameter Design
Computer Experiment
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