Development of an algorithm to train artificial neural networks for intelligent decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.192711

Keywords:

artificial neural networks, synaptic weights, membership function, information processing, intelligent decision support systems

Abstract

The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership function occurs taking into consideration the computation resources of the facility and taking into consideration the type and the amount of information entering the input of an artificial neural network. In addition, when using the proposed algorithm, there is no accumulation of an error of artificial neural networks training as a result of processing the information entering the input of artificial neural networks.

Development of the proposed algorithm was predetermined by the need to train artificial neural networks for intelligent decision support systems in order to process more information given the unambiguity of decisions being made. The research results revealed that the specified training algorithm provides on average 16–23 % higher the efficiency of training artificial neural networks training that is on average by 16–23 % higher and does not accumulate errors in the course of training. The specified algorithm will make it possible to conduct training of artificial neural networks; to determine effective measures to enhance the efficiency of functioning of artificial neural networks. The developed algorithm will also enable the improvement of the efficiency of functioning of artificial neural networks due to training the parameters and the architecture of artificial neural networks. The proposed algorithm reduces the use of computational resources of decision support systems. The application of the developed algorithm makes it possible to work out the measures aimed at improving the effectiveness of training artificial neural networks and to increase the efficiency of information processing

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 Research, Head of Department

Department of Automated Control Systems

Oleksandr Turinskyi, Ivan Kozhedub Kharkiv University of Air Force Sumska st., 77/79, Kharkiv, Ukraine, 61023

PhD, Head of University

Andrii Shyshatskyi, Central Scientifically-Research Institute of Arming and Military Equipment of the Armed Forces of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03168

PhD, Senior Research

Research Department of Electronic Warfare Development

Volodymyr Dudnyk, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79026

PhD

Department of Fire Training

Ruslan Zhyvotovskyi, Central Scientifically-Research Institute of Arming and Military Equipment of the Armed Forces of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03168

PhD, Senior Researcher, Head of Department

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

Yevgen Prokopenko, Ivan Chernyakhovsky National Defense University of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03168

PhD

Institute of Information Technology

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

PhD, Associate Professor

Valerii Hordiichuk, Institute of Naval Forces National University “Odessa Maritime Academy” Hradonachalnytska str., 20, Odessa, Ukraine, 65029

PhD, Head of Department

Scientific and Organizational Department

Anton Nikitenko, Ivan Chernyakhovsky National Defense University of Ukraine

Adjunct

Institute of Public Military Management

Artem Remez, Ivan Chernyakhovsky National Defense University of Ukraine Povitrofloskyi ave., 28, Kyiv, Ukraine, 03049

Adjunct

Institute of Public Military Management

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Published

2020-02-03

How to Cite

Sova, O., Turinskyi, O., Shyshatskyi, A., Dudnyk, V., Zhyvotovskyi, R., Prokopenko, Y., Hurskyi, T., Hordiichuk, V., Nikitenko, A., & Remez, A. (2020). Development of an algorithm to train artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 1(9 (103), 46–55. https://doi.org/10.15587/1729-4061.2020.192711

Issue

Vol. 1 No. 9 (103) (2020): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2020 Oleg Sova, Oleksandr Turinskyi, Andrii Shyshatskyi, Volodymyr Dudnyk, Ruslan Zhyvotovskyi, Yevgen Prokopenko, Taras Hurskyi, Valerii Hordiichuk, Anton Nikitenko, Artem Remez

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