Desulfurization process using Takagi–Sugeno–Kang fuzzy modeling

Two Takagi–Sugeno–Kang fuzzy models for the prediction of the amount of reagents for desulfurization in steel processing are developed from experimental data. For the design of the models, an algorithm was proposed to be used in the procedures of the two phases: structure building and parametric identification. In the first phase, the Gustafson–Kessel clustering algorithm with the cluster validity index was proposed to find the number of fuzzy rules and an initial fuzzy model. In the second phase, a gradient-descent-based approach was developed and used for optimized tuning of membership functions of the fuzzy model. The numerical results were compared with a conventional statistical model and neural networks and adaptive network-based fuzzy inference system.