Endpoint prediction model of basic oxygen furnace steelmaking based on robust relevance-vector-machines
基于鲁棒相关向量机的转炉炼钢终点预报模型

To deal with the problem that the classical relevance vector machine is sensitive to outliers, we present a novel robust relevance vector machine. This machine is applied to predict the endpoint carbon content and temperature of the basic-oxygen-furnace(BOF) steelmaking. Each training sample is assumed to have its individual coefficient of noise variance. With the increase of the prediction error during training procedure, the coefficients of outliers gradually decrease, reducing the impact of outliers. In addition, the iterative formulas for the optimization of hyper-parameters are derived in the Bayesian evidence framework. Simulation results of benchmark test data and the BOF steelmaking data show that the proposed mode achieves high prediction accuracy and good robustness.