Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace

摘要

In this study, a novel prediction model, hybrid of mechanism method, Takagi-Sugeno (T-S) fuzzy modeling, regularization network technique and Multi-kernel learning algorithm, is proposed for accurately forecasting the liquid steel temperature in ladle furnace (LF). By virtue of mechanism method and T-S fuzzy modeling technique, a partial linear structured mechanism model is firstly obtained, which contains a parametric linear part with unknown coefficients and a nonparametric part with unknown functional expression. Thereafter, it is parameterized and implemented by a modified regularization network, called partial linear regularization network (PLRN), which introduces a parametric linear part into the traditional regularization network. Furthermore, to optimally design the kernel of PLRN and thereby further improve the prediction performance, Multi-kernel learning approach is employed to obtain the so called Multi-kernel learnt PLRN. The principal innovation behind the proposed method is the embedding of the prior knowledge into the model, instead of directly predicting the steel temperature using machine learning techniques which is commonly used in the previous steel temperature prediction models. This innovation leads to better final results in reducing the model complexity, improving the generalization performance and consequently promoting the prediction precise. The experiment results demonstrate that the novel predictor is superior in prediction performance over other black-box based predictors. Furthermore, the prediction accuracy is boosted via Multi-kernel learning.