Correntropy-based kernel learning for nonlinear system identification with unknown noise: an industrial case study

Abstract

One significant challenge in nonlinear system identification development for industrial processes is that the modeling samples often contain outliers and unknown noise. In this paper, a novel Correntropy-based Kernel Learning (CKL) method is proposed for identification of nonlinear systems with such uncertainty. Without resort to unnecessary efforts, the CKL identification method can reduce the effects of outliers by the use of a robust nonlinear estimator that maximizes correntropy. The superiority of the proposed CKL method is demonstrated through identification of an industrial process in Taiwan. The benefit of its more accurate and reliable performance indicates that CKL is promising in practice for identification of nonlinear systems with unknown noise.