Development of a methodology for training artificial neural networks for intelligent decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.199469

Keywords:

artificial neural networks, training, efficiency, information processing, intelligent decision support systems

Abstract

The method of training artificial neural networks for intelligent decision support systems is developed. A distinctive feature of the proposed method is that it provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. If it is impossible to provide the specified quality of functioning of artificial neural networks due to the learning of the parameters of the artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the computing resources of the tool and taking into account the type and amount of information supplied to the input of the artificial neural network. Due to the use of the proposed methodology, there is no accumulation of errors of training artificial neural networks as a result of processing information that is fed to the input of artificial neural networks. Also, a distinctive feature of the developed method is that the preliminary calculation data are not required for data calculation. The development of the proposed methodology is due to the need to train artificial neural networks for intelligent decision support systems in order to process more information with the uniqueness of decisions made. According to the results of the study, it is found that the mentioned training method provides on average 10–18 % higher efficiency of training artificial neural networks and does not accumulate errors during training. This method will allow training artificial neural networks through the learning of parameters and architecture, identifying effective measures to improve the efficiency of artificial neural networks. This methodology will allow reducing the use of computing resources of decision support systems and developing measures aimed at improving the efficiency of training artificial neural networks; increasing the efficiency of information processing in artificial neural networks

Author Biographies

Oleg Sova, Military Institute of Telecommunications and Informatization named after Heroes of Kruty Moskovska str., 45/1, Kyiv, Ukraine, 01011

Doctor of Technical Sciences, Senior Researcher, Head of Department

Department of Automated Control Systems

Andrii Shyshatskyi, Central Scientifically-Research Institute of Arming and Military Equipment of the Armed Forces of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

PhD, Senior Researcher

Research Department of Electronic Warfare Development

Yurii Zhuravskyi, Zhytomyr Military Institute named after S. P. Koroliov Myru ave., 22, Zhytomyr, Ukraine, 10004

Doctor of Technical Sciences, Senior Researcher

Scientific Center

Olha Salnikova, National Defense University of Ukraine named after Ivan Cherniakhovskyi Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

Doctor of Public Administration, Senior Researcher, Head of the Educational and Research Center

Educational and Research Center of Strategic Communications in the sphere of National Security and Defense

Oleksandr Zubov, National Defense University of Ukraine named after Ivan Cherniakhovskyi Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

PhD, Associate Professor

Department of Troop Control

Ruslan Zhyvotovskyi, Central Scientifically-Research Institute of Arming and Military Equipment of the Armed Forces of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

PhD, Senior Researcher, Head of Research Department

Research Department of the Development of Anti-Aircraft Missile Systems and Complexes

Іgor Romanenko, Central Scientifically-Research Institute of Arming and Military Equipment of the Armed Forces of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

Doctor of Technical Sciences, Professor, Leading Researcher

Research Department of the Development of Anti-Aircraft Missile Systems and Complexes

Yevhen Kalashnikov, National Defense University of Ukraine named after Ivan Cherniakhovskyi Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

PhD, Head of Research Laboratory

Research Laboratory of Problems of Development of Combat Use of Rocket Forces and Artillery

Artem Shulhin, State Aviation Research Institute Andryushchenko str., 6V, Ukraine, 01135

PhD, Senior Researcher

Research Department

Alexander Simonenko, Military Institute of Telecommunications and Informatization named after Heroes of Kruty Moskovska str., 45/1, Kyiv, Ukraine, 01011

Senior Lecturer

Department of Automated Control Systems

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Published

2020-04-30

How to Cite

Sova, O., Shyshatskyi, A., Zhuravskyi, Y., Salnikova, O., Zubov, O., Zhyvotovskyi, R., Romanenko І., Kalashnikov, Y., Shulhin, A., & Simonenko, A. (2020). Development of a methodology for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 2(4 (104), 6–14. https://doi.org/10.15587/1729-4061.2020.199469

Issue

Vol. 2 No. 4 (104) (2020): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2020 Oleg Sova, Andrii Shyshatskyi, Yurii Zhuravskyi, Olha Salnikova, Oleksandr Zubov, Ruslan Zhyvotovskyi, Іgor Romanenko, Yevhen Kalashnikov, Artem Shulhin, Alexander Simonenko

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