Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill

Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule-base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a real-life mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The performance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve temperature estimation; however, the best results are obtained when fuzzy C-means algorithm, grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.