Research of possibilities of using neural networks in the decision support system

Authors

DOI:

https://doi.org/10.15587/1729-4061.2015.47692

Keywords:

decision support system, neural networks, multi-assortment dairy production

Abstract

The possibility of using neural networks in automated control systems for multi-assortment dairy production was considered. In the automatic control theory, many methods were developed that allow to optimize systems in terms of one or another quality criteria, provided that the number of restrictions is fulfilled, but mathematical tools used in the traditional automatic control methods, are not always able to fully ensure satisfactory results under a limited number of input data. Using neural networks allows to perform control of acceptable quality (not necessarily optimal) under uncertainty at a relatively low level of resources spent.

During the research, the structure and learning algorithm of the neural network for the decision support system concerning the dairy plant assortment forecasting for the current day was determined. In the intelligent technology environment STATISTICA NeuralNetwork based on the model obtained, the sensitivity analysis of fuzzy neural network output to a change in the input stream was carried out. Using the neural network allows to take into account nonlinear dependences in problems of forecasting profitable dairy plant assortment, which is important for effective management under uncertainty.

The research conducted are needed to develop the automated control system for multi-assortment dairy production.

Author Biographies

Ольга Вікторівна Савчук, National University of Food Technologies 68 Vladimir str., Kyiv, Ukraine, 01601

Graduate student, assistant

Department of automation of management processes

Анатолій Петрович Ладанюк, National University of Food Technologies 68 Vladimir str., Kyiv, Ukraine, 01601

Professor, Doctor of technical sciences, Head of Department

Department of automation of management processes

References

  1. Tarasov, V., Gerasimov B., Levin, I., Korniychuk, V. (2007). Intelligent Decision Support System: Theory, synthesis efficiency. Kiev: MAKNS, 335.
  2. Stetsenko, D. (2013). Development of intelligent control algorithms brahorektyfikatsiynoyu installation. Тehnology audit and production reserves, 6/1 (14), 51-54. Available at: http://journals.uran.ua/tarp/article/view/19551/17224
  3. Zihunov, A., Kyshenko, V., Belyaev, Y. (2013). Neural models of detection and recognition technology situations. Scientific and technical information, 1 (55), 72–78.
  4. Stetsenko, D., Zihunov, O., Smityuh, Y. (2014). Data mining system for automated control of technological complex brahorektyfikatsiyi. Тehnology audit and production reserves, 2/1 (16), 49–52. doi: 10.15587/2312-8372.2014.23452
  5. Sidlec'kyj, V. M., Elperin, I. V. (2014). Forecasting system performance of diffusion plant sugar factory. Eastern-European Journal of Enyerprise Technologies, 3/3 (51), 8–11. Available at: http://journals.uran.ua/eejet/article/view/1504/1402
  6. Jarrett, K., Kavukcuoglu, K., Ranzato, M. (2009). What is the best multi-stage architecture for object recognition? 2009 IEEE 12th International Conference on Computer Vision, 2146–2153. doi: 10.1109/iccv.2009.5459469
  7. Lee, H., Grosse, R., Ranganath, R. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning – ICML '09, 609–616. doi: 10.1145/1553374.1553453
  8. Gladun, V., Velichko, V. (2012). Instrument complex support adoption of solutions based on Network model predmetnoy region: Coll. reported. scientific-practic. Conf. with international participation "Decision Support Systems. Theory and Practice ". Kiev, 126–128.
  9. Savchuk, O., Ladanyuk, A., Gerasimenko, T. (2015). Fuzzy cognitive modeling in complex systems of technological milk processing. New University of Engineering, 1-2 (35-36), 13–19.
  10. Nazarov, A., Loskutov, А. (2007). Neural network algorithms for prediction and optimization of systems. SPb. Science and Technology, 384.
  11. Haykin, S. (2006). Neural networks: a complete course. 2nd Edition. Trans. from English. Moscow: Publishing House "Williams", 1104.
  12. Korchemnaya, M., Lysenko, V., Chapni, M. (2008). NEURAL NETWORKS. Kiev: of NAU, 156.
  13. Borovikov, V. (2008).Neural Networks. STATISTICA Neural Networks: Methodology and technology of modern data analysis. Second edition.Moscow: Hotline Telecom, 392.

Published

2015-08-27

How to Cite

Савчук, О. В., & Ладанюк, А. П. (2015). Research of possibilities of using neural networks in the decision support system. Eastern-European Journal of Enterprise Technologies, 4(4(76), 15–19. https://doi.org/10.15587/1729-4061.2015.47692

Issue

Section

Mathematics and Cybernetics - applied aspects