Mikhaylova N.A., Timokhin D.V. NEURAL NETWORKS AS ATOOL FOR THE CONVERGENCE DEVELOPMENT OF INNOVATIVE PRODUCTIONS
DOI: https://doi.org/10.15688/ek.jvolsu.2023.2.8
Nataliya A. Mikhaylova
Candidate of Sciences (Economics), Associate Professor, Department of Financial-Economic, Logistical and Medical Support, Academy of Management of the Ministry of Internal Affairs of Russia, Zoi i Aleksandra Kosmodemyanskikh St, 8, 127171 Moscow, Russian Federation, This email address is being protected from spambots. You need JavaScript enabled to view it. , https://orcid.org/0000-0002-3747-3587
Dmitriy V. Timokhin
Candidate of Sciences (Economics), Associate Professor, Department of Economics and Innovation, Moscow State University of Humanities and Economics, Losinoostrovskaya St, 49, 107150 Moscow, Russian Federation, This email address is being protected from spambots. You need JavaScript enabled to view it. , https://orcid.org/0000-0002-5716-6699
Abstract. The main trend in the development of the sectoral structure of the post-industrial economies of the world is the technological convergence of previously isolated industries on the basis of the emerging technological platforms of the national and global economies. Technological platforms are gradually replacing the economic niche previously occupied by clusters and ensuring the creation of a synergistic effect from the interaction of diverse industry participants. The greatest number of innovative solutions in such a situation is intersectoral in nature, which is accompanied by the replacement of separate industry producers by a united convergent manufacturer. The process of closing heterogeneous industry technological cycles within the framework of the formation of innovative conversion production can be described as a model, the demand for which by industry analysts has increased significantly since the beginning of the 21st century. At the same time, the complexity of industry value chains requires the modernization of the existing analytical tools for industry design. The most promising tool for designing innovative conversion production is neural networks. In the framework of this article, the author conducted a study of the potential of neural networks as a tool for designing and modernizing the “economic cross” of sectoral conversion industries and assessed the prospects for expanding the use of this tool, taking into account the actualization of neural networks as an investment object from the end of 2022 to the beginning of 2023.
Key words: branch economics, innovative economics, economic modeling, neural networks, digitalization, technological platforms.
NEURAL NETWORKS AS ATOOL FOR THE CONVERGENCE DEVELOPMENT OF INNOVATIVE PRODUCTIONS by Mikhaylova N.A., Timokhin D.V. is licensed under a Creative Commons Attribution 4.0 International License.
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