Intellectual data analyzing in automated management system of bragorectification settin

Authors

DOI:

https://doi.org/10.15587/2312-8372.2014.23452

Keywords:

bragorektification setting, data mining, neuro-uncertain technology, automatic analysis

Abstract

This article tells about Data Mining technology using for analyzing data and extract information from data set on the example of  bragorectification setting. The main task of exploration is the automatic analysis of  methods and systems, data connections developping between amounts which can be seen as a kind of summary of the input data and may be used in further analysis, modeling or forecasting. This article considers  the main influencing factors of technological processes and interconnection between input and output data based on Bragorectification setting  operation (BRS). One of such methods is neuro – uncertain  technology. To achieve the aim we have got information and statictics about management object operation and controlling. It was built parametric structure of Bragorectification setting  neuro – uncertain  model. It was formulated uncertain structure data base model and received the surface response as graphical dependencies for operator decisions. Using these methods of information processing in sub- decisions, the effectiveness of Bragorectification setting  control will significantly increase.

Author Biographies

Дмитро Олексійович Стеценко, National University of Food Technologies, Str. Vladimirskaya, 68, Kyiv, Ukraine, 01033

Graduate student

Department of automation of management processes

Олександр Михайлович Зігунов, Sumy Regional Training and Scientific Center, National University of Food Technologies,

Candidate of Engineering Sciences, Associate Professor

Ярослав Володимирович Смітюх, National University of Food Technologies, Str. Vladimirskaya, 68, Kyiv, Ukraine, 01033

Candidate of Engineering Sciences, Associate Professor

Department of automation of management processes

References

  1. Стабников, В. Н. Ректификация в пищевой промышленности. Теория процесса, машины, интенсификация [Текст]/ В. Н. Стабников, А. П. Николаев, М. Л. Мандельштейн. – М.: Легкая и пищевая промышленность, 1982. – 232 с.
  2. Мандельштейн, М. Л. Автоматические системы управления технологическим процессом брагоректификации [Текст]/ М. Л. Мандельштейн. – М.: Пищевая промышленность, 1975. – 240 с.
  3. Мандельштейн, М. Л. Математическая модель и статические характеристики ректификационной колонны [Текст]/ М. Л. Мандельштейн. – Ферментная и спиртовая промышленность. – 1969. – №1. – С. 11-16.
  4. Петренко, А. І. GRID та інтелектуальна обробка даних DATA MINING [Текст]/ А. І. Петренко// System Research & Information Technologies. – 2008. – № 4. – P. 97-110.
  5. Romero, C. Data mining in course management systems: Moodle case study and tutorial [Text]/ C. Romero, S. Ventura, E. García// Computers & Education. – 2008. – Vol. 57(1). – P. 368-384.
  6. Зігунов, О. М. Нейромережеві моделі виявлення і розпізнавання технологічних ситуацій [Текст]/ О. М. Зігунов, В. Д. Кишенько, Ю. Б. Бєляєв// Науково-технічна інформація. – 2013. – №1(55). – С. 72-78.
  7. Chrysostomou, K. Investigation of Users` Preferences in Interactive Multimedia Learning Systems: A Data Mining Approach [Text]/ K. Chrysostomou, S. Y. Chen, X. Liu// Interactive Learning Environments. – 2009. – Vol. 17(2). – Р. 151-163.
  8. Larsen, K. R. Analyzing unstructured text data: Using latent categorization to identify intellectual communities in information systems [Text]/ Kai R. Larsen, David E. Monarchi, Dirk S. Hovorka, Christopher N. Bailey// Decision Support Systems. – 2008. – Vol. 45, Issue 4. – P. 884-896.
  9. Ротштейн, А. П. Интеллектуальные технологии идентификации: нечеткая логика, генетические алгоритмы, нейронные сети [Текст]/ А. П. Ротштейн. – Винница: Универсум-Винница, 1999. – 320 с.
  10. Jang, J.-S. R. ANFIS: Adaptive-Network-Based Fuzzy Inference System [Text]/ J.-S. R. Jang// IEEE Trans. Systems & Cybernetics. – 1993. – Vol. 23. – P. 665-685.
  11. Stabnikov, V. N., Nikolaev, A. P., Mandel'shtein, M. L. (1982). Rektifikatsiia v pishchevoi promyshlennosti. Teoriia protsessa, mashiny, intensifikatsiia. M.: Lehkaia i pishchevaia promyshlennost', 232.
  12. Mandel'shtein, M. L. (1975). Avtomaticheskie sistemy upravleniia tekhnolohicheskim protsessom brahorektifikatsii. M.: Pishchevaia promyshlennost', 240.
  13. Mandelshtejn, M. L. (1969). Matematicheskaya model i staticheskie xarakteristiki rektifikacionnoj kolonny. Fermentnaya i spirtovaya promyshlennost, 1, 11-16.
  14. Petrenko, A. I. (2008). GRID ta іntelektualna obrobka danix data mining. System research & information technologies, 4, 97-110.
  15. Romero, C., Ventura, S., García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 57(1), 368-384.
  16. Zіgunov, O. M., Kishenko, V. D., Belyaev, Yu. B. (2013). Nejromerezhevі modelі viyavlennya і rozpіznavannya tekhnologіchnikh situacіj. Naukovo-tekhnіchna іnformacіya, 1(55), 72-78.
  17. Chrysostomou, K., Chen, S. Y., & Liu, X. (2009). Investigation of Users` Preferences in Interactive Multimedia Learning Systems: A Data Mining Approach. Interactive Learning Environments, 17(2), 151-163.
  18. Larsen, K. R., Monarchi, D. E., Hovorka, D. S., Bailey, Ch. N. (2008). Analyzing unstructured text data: Using latent categorization to identify intellectual communities in information systems. Decision Support Systems, 45(4), 884-896.
  19. Rotshtejn, A. P. (1999). Intellektualnye texnologii identifikacii: nechetkaya logika, geneticheskie algoritmy, nejronnye seti. Vinnica.: Universum-Vinnica, 320.
  20. Jang, J.-S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. Systems & Cybernetics, 23, 665-685.

Published

2014-03-18

How to Cite

Стеценко, Д. О., Зігунов, О. М., & Смітюх, Я. В. (2014). Intellectual data analyzing in automated management system of bragorectification settin. Technology Audit and Production Reserves, 2(1(16), 49–52. https://doi.org/10.15587/2312-8372.2014.23452