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.
Full Text:PDF (Українська)
Kaganov V. Yu., Blinov O. M., Belen’kij A. M.
Avtomatizaciya upravleniya metallurgicheskimi
protsessami. Moscow, Metallurgy, 1974, 416 p.
Nelles O. Nonlinear System Identification: From Classical
Approaches to Neural and Fuzzy Models. Berlin, Springer,
, 785 p.
Ivakhnenko A. G. Induktivnyj metod samoorganizacii
modelej slozhnyh sistem. Kyiv, Naukova dumka, 1981,
Rutkovskaya D., Pylyn’skyy M., Rutkovskyy L. Nejronnye
seti, geneticheskie algoritmy i nechetkie sistemy. Moscow,
Hotline-Telecom, 2006, 452 p.
Yager R., Filev D. Essentials of Fuzzy Modelling and
Control. USA, John Wiley & Sons, 1984, 387 p.
Shtovba S. D. Proektyrovanye nechetkykh system
sredstvamy Matlab. Moscow, Hotline-Telecom, 2007,
Bezdek J. C. Pattern Recognition with Fuzzy Objective
Function Algorithms. New York, Plenum Press, 1981,
Kornienko V., Gusev A., Gerasina A. Methods and
principles of control over the complex objects of mining and
metallurgical production, Energy Efficiency Improvement of
Geotechnical Systems. London, CRC Press, Taylor &
Francis Group, 2013, pp. 183–192.
Korniienko V. I., Gulina I. G., Budkova L. V. Complex
estimation, identification and prediction of difficult
nonlinear processes, Naukovyi visnyk Natsionalnogo
girnychogo universyteta, 2013, No. 6, pp. 124–131.
Кruglov V. V., Dli М. I., Golunov К. Yu. Nechetkaya logika
i iskusstvennye nejronnye seti. Moscow, Fizmatlit, 2001,
Panteleev A. V., Letova T. A. Metody optimizacii v
primerah i zadachah. Moscow, High school, 2005, 544 p.
Ivakhnenko A. G., Madala H. R. Inductive learning
algorithms for complex systems modeling. London, Tokyo,
CRC Press, 1994, 384 p.
Van der Waerden B.L. Matemeticheskaya statistika.
Moscow, Foreign Literature, 1960, 436 p
GOST Style Citations
Copyright (c) 2019 O. V. Herasina, O. Yu. Husiev, V. I. Korniienko
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Address of the journal editorial office:
Editorial office of the journal «Radio Electronics, Computer Science, Control»,
National University "Zaporizhzhia Polytechnic",
Zhukovskogo street, 64, Zaporizhzhia, 69063, Ukraine.
Telephone: +38-061-769-82-96 – the Editing and Publishing Department.
The reference to the journal is obligatory in the cases of complete or partial use of its materials.