Non-iterative T-S fuzzy modeling with random hidden-layer structure for BFG pipeline pressure prediction

The pressure of blast furnace gas (BFG) pipeline is one of the most significant criteria for the scheduling of byproduct gas system in steel industry. Given the measuring problems under current industrial conditions, a non-iterative T-S fuzzy model is established in this study for on-line modeling. Firstly, a random hidden-layer structure for the antecedent part of fuzzy rules is designed for fuzzy partitioning, which improves the efficiency of on-line training and makes the model adaptive for industrial application. A fast sparse coding identification method is then proposed to determine the consequent parameters of the fuzzy rules, which is capable of enhancing the robustness and the generalization performance under various production situations. To verify the effectiveness of the proposed method, the noise additive Mackey-Glass time series data and the practical data coming from a steel plant are employed. Furthermore, the statistics results from the practice indicate that the proposed method has good adaptability and can provide reliable measurement results for industrial applications.