Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace - Zhou, Ping; Zhao, Peng; Ou, Zihui; Chai, Tianyou (Neural computing & applications, 2025)
This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten iron quality (MIQ) in blast furnace (BF) ironmaking. First, in order to accurately describe the importance of different process variables on the multivariate MIQ indices, an attention mechanism based on feedforward compensation is introduced. The mechanism is embedded at the front end of the stacked semi-supervised autoencoder deep network. Secondly, to extract the deep feature information which is closely related to the prediction target from the process data, a deep network structure composed of multiple semi-supervised autoencoder models is introduced. The output of the last hidden layer is then used as the input of the subsequent prediction model. At the same time, two regularization terms L 1 and L 2 are incorporated into RVFLNs to sparse network output weights and improve the robustness of modeling, aiming to solve the multicollinearity and overfitting problems of the basic RVFLNs. Experiments using the standard dataset and actual industrial data of BF demonstrate that the proposed method has good performance.