Incremental Huber-Support vector regression based online robust parameter design

Abstract

In the response surface based RPD, the optimal setting of the controllable factor is highly dependent on the accuracy of the response surface. Classically, in order to improve the accuracy of the response surface, it is necessary to add more samples. The larger the number of samples, the higher the accuracy. Traditional RPD usually uses a one-shot modeling method to construct a response surface. Whenever the number of samples increases, all samples need to be learned from the beginning to rebuild the response surface. However, The one-shot modeling method significantly increases the time of model training and the complexity of model training. We present an incremental strategy to build response models. Our solution is based on the Huber-support vector regression machine. In this article, the incremental Huber-SVR model is proposed to construct the response surface in robust parameter design. The proposed algorithm can continuously integrate new sample information into the already built model. In incremental HSVR-RPD, we can use the optimal settings of the previous controllable factors, the currently observed noise factor and the corresponding response to improve the accuracy of the response surface, so as to obtain more reliable recommended settings in the next stage.