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Assessment of the industrial policy to the military-industrial complex effectiveness based on neural networks based on fuzzy logic

Authors :
E.N. Starikov
N.V. Klein
V.I. Vorobyov
Source :
Цифровые модели и решения, Vol 3, Iss 2, Pp 43-54 (2024)
Publication Year :
2024
Publisher :
Ural State University of Economics, 2024.

Abstract

The relevance of the topic under consideration on the development of universal applied methods for assessing the effectiveness and efficiency of industrial policy in the military-industrial complex (hereinafter - MIC) is due to its high practical significance in the context of the challenges of technological development associated with digitalization and informatization. It is also due to the peculiarities of the current stage of Russia’s economic development in the conditions of financial and technological sanctions from Western states. The purpose of the study is to develop the main provisions of a methodological approach to assessing the effectiveness of industrial policy in the defense industry based on the use of economic-mathematical modeling apparatus, which involves the construction of neural networks based on fuzzy logic. In the course of the research the authors have solved the following problems: mathematically formalized the object of analysis; developed an algorithm for determining the effectiveness of industrial policy in the MIC using neural networks; formalized the model for assessing the effectiveness of such industrial policy based on fuzzy sets; proposed a system of indicators for assessing industrial policy in the MIC; determined the sequence of actions in the construction the fuzzy model for assessing the effectiveness of industrial policy in the MIC by means of Fuzzy Logic on the software platform MatLab.

Details

Language :
English, Russian
ISSN :
2949477X and 27824934
Volume :
3
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Цифровые модели и решения
Publication Type :
Academic Journal
Accession number :
edsdoj.33d7829bf3a948de8a96ce0417da4c75
Document Type :
article
Full Text :
https://doi.org/10.29141/2949-477X-2024-3-2-4