Ladle Furnace Steel Temperature Prediction Model Based on Partial Linear Regularization Networks with Sparse Representation

Partial linear regularization networks (PLRN) combined with sparse representation technique is developed to establish the steel temperature prediction model for LF. Parametric linear part is introduced into the classical regularization networks in order to fit the partial linear structured temperature model, which is obtained by analyzing the mechanism of LF thermal system in detail. Improvement in prediction accuracy is achieved due to the well learning performance of regularization networks and the modification according to the special structure. Furthermore, sparse representation technique is adopted on original PLRN for the sake of reducing computational cost and further improving the generalization performance. Learning scheme of recursive version is designed to train the sparsely represented PLRN, in which support vectors is selected one-by-one and recursive algorithm is adopted for computational efficiency. The proposed method is examined by practical data. The experiment results demonstrate that the proposed method can both improve the prediction accuracy and lead to sparse solution, so that it reduce the storage need and the prediction time for practical application.