Issue |
SHS Web Conf.
Volume 65, 2019
The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)
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Article Number | 04004 | |
Number of page(s) | 6 | |
Section | Mathematical Methods, Models, Informational Systems and Technologies in Economics | |
DOI | https://doi.org/10.1051/shsconf/20196504004 | |
Published online | 29 May 2019 |
Neural network and index forecasting of the strategies of development of the armed forces of Ukraine depending on their own economic opportunities and encroachments of the states of aggressors
1
Classical Private University, Zaporizhzhia, Ukraine
2
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
* Corresponding author: vprhnp76@gmail.com
Ukraine has a relative drawback in the economic defense capabilities, which needs to be addressed by raising the indicators of macroeconomic development, innovation, and economic potential, social health of the population of the state, and the support of the Armed Forces of Ukraine, by the state. The estimation of the defense capability of states like Ukraine, Poland, Russia and Turkey is made on the basis of the developed methodological approach to the overall representation of the health of the economies of the states and their defense capabilities using the method of constructing petal diagrams with the definition of their effective areas, which became indicators of economic status and defense capability. The article analyses the dependence of the development level of the countries’ economies and the state of development of the armed forces of these countries in the conditions of resource constraints and existing risks on the basis of macroeconomic data and indicators. This article uses the indicators for the determination of the level of defense capability and the data of petal diagrams and the scenario modeling of the development strategies of the Armed Forces of Ukraine with the aim of constructing the most optimal forecast in this area.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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