Corrigendum to 'Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace' [Knowl.-Based Syst. 36 (2012) 280-287]

摘要

We published a hybrid model for forecasting the ladle furnace (LF) liquid steel temperature in Knowledge-Based Systems [1] as an extension of the predictor presented in Steel Research International [2]. The fundamental of LF thermal modeling and partial linear regularization network (PLRN) algorithm are the basis of the completed model development and therefore introduced in both [1,2] with minor amendment. As one of the factors affecting the temperature variation, temperature change ΔT_(Arc) caused by arc heating was initially described in a nonparametric way in [2], while in [1 ] it was parameterized by T-S fuzzy method with embedding its prior knowledge, thereby improving the prediction accuracy. Moreover, Section 3 in [1] proposed a novel partial linear fitting method, termed multi-kernel learnt partial linear regularization network (MKL-PLRN), to fit the hybrid model. This MKL-PLRN employed multi-kernel learning (MKL) method to optimize the kernel function of PLRN presented in [2].