NEURO-FUZZY FORECASTING OF NON-LINEAR PROCESSES OF BLAST FURNACE PRODUCTION
Context. Neuro-fuzzy forecasting of the chemical composition of cast iron at the blast furnace output to improve the quality of blast furnace production control is considered.
Objective. The aim of the work is to reduce the errors of forecasting non-linear processes of blast-furnace production.
Method. It was proposed to use neural-fuzzy adaptive filter-approximators for forecasting non-linear processes of blast-furnace production (in the form of: Adaptive neuro-fuzzy inference system, fuzzy algorithm of subtractive clustering and fuzzy C-means clustering algorithm), which realize sequential and step-by-n step integration of current information. To optimize these filters for real processes, their parameters are identified by the accuracy criterion on the training and verification sequences.
Results. As a result of the simulation of neural-fuzzy forecasting of the content of the chemical composition of cast iron at the blast furnace output, it was found that the best accuracy is provided by a fuzzy filter with subtractive clustering with sequential integration of the current data. At the same time, the forecast error is 4.2%, and the time for finding the optimal solutions does not
introduce time restrictions on the application of this approach in blast-furnace production. The adequacy of the data was confirmed.
Conclusions. Neural-fuzzy filters allow to increase the accuracy of the forecast of non-linear processes of blast furnace smelting and, due to this, to improve the quality of management of the production of cast iron. Further research should be directed to the development of automatic control systems for non-linear processes of blast-furnace production.
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