Rule Extraction From Fuzzy-Based Blast Furnace SVM Multiclassifier for Decision-Making

Abstract

Black-box models play an important role in advancing the blast furnace modeling technologies for control purposes. To further enhance their practical applications, this paper is concerned with the transparency and comprehensibility of blast furnace black-box models. A fuzzy-based support vector machine (SVM) classification algorithm is proposed to perform the tasks of determining the controllable bound from the real data, of reducing feature from extensive candidate inputs, and of training the SVM model parameters. Based on these results, a fuzzy-based blast furnace SVM three-class classifier is constructed to serve for the classification problem according to the output lower than its controlled bound, within the controlled bound and higher than the controlled bound. Further, rule extraction is made to achieve the understandability of the constructed SVM classifier. Through two typical real blast furnace cases, the extracted rules can work well in classifying the hot metal silicon content into low, proper, and high range with high transparency, as well as encouraging agreements between the predicted values and the real ones. Moreover, there needs to be very little information on the blast furnace variables when implementing every rule in practice. The extracted rules provide a more explicit and direct indication for the blast furnace operators and, thus, may serve better for decision-making with blast furnace control.