Neuromathematics as an effective tool for forecasting social development of Russian regions
R.V. Gubarevb, E.I. Dzyubaa
aPlekhanov Russian University of Economics, Moscow, 115054 Russia
bOffice of the All-Russia People's Front in the Republic of Bashkortostan, Ufa, 450077 Russia
Received December 27, 2018
DOI: 10.26907/2541-7746.2019.2.315-321
For citation: Gubarev R.V., Dzyuba E.I. Neuromathematics as an effective tool for forecasting social development of Russian regions. Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, 2019, vol. 161, no. 1, pp. 315–321. doi: 10.26907/2541-7746.2019.2.315-321.
Abstract
In the context of the national economic turbulence, it becomes important to forecast the social development of constituent entities of the Russian Federation. In order to provide highly accurate forecasting, neural network technologies are used in the research (a Bayesian assembly of the dynamic neural network of various configurations is formed). As a result of the forecasting, it is found, that the leading Russian regions should have a lower social development index in 2016–2017 as compared to 2014–2015. A slowdown of social development is also predicted for the leading regions of the Volga Federal District in 2016–2017, but only as compared to 2015. The obtained data show that the social development index in the Republic of Bashkortostan changes a little. Nevertheless, a significant lagging of Bashkortostan behind the leading regions of the Russian Federation and the Volga Federal District in the social sphere is predicted: Bashkortostan is a competitive region in terms of the living standards, but not in the sphere of scientific research and innovations. For this reason, measures encouraging innovative development of Russian regions as exemplified by the Republic of Bashkortostan are introduced and discussed in the paper.
Keywords: forecasting social development, Russian regions, neural simulation, Bayesian assembly of neural networks
Acknowledgements. The study was supported by the Russian Foundation for Basic Research (project no. 19-010-00067).
References
1. Beloliptsev I.I., Gorbatkov S.A., Romanov A.N., Farkhievaa S.A. Modelirovanie upravlencheskikh reshenii v sfere ekonomiki v usloviyakh neopredelennosti [Modeling Managerial Decisions in the Sphere of Economy under the Conditions of Uncertainty]. Moscow, INFRA-M, 2015. 299 p. (In Russian)
2. Bukaev G.I., Bublik N.D., Gorbatkov S.A., Satarov R F. Modernizatsiya sistemy nalogovogo kontrolya na osnove neirosetevykh informatsionnykh tekhnologii [Upgrading the Tax Control on the Basis of Information Technologies of Neural Networks]. Moscow, Nauka, 2001. 341 p. (In Russian)
3. Golovko V.A. Neural networks: Training, organization, and application. In: Neirokomp'yutery i ikh primenenie [Neurocomputers and Their Application]. IPRZhR, 2001. 256 p. (In Russian)
4. Cherednichenko L.G., Gubarev R.V., Dzyuba E.I., Faizullin F.S. Assessing social development of Russian regions. Finans.: Teor. Prakt., 2018, vol. 22, no. 5, pp. 132–152. doi: 10.26794/2587-5671-2018-22-6-132-152. (In Russian)
5. Rissanen J. Modeling by shortest data description. Automatica, 1978, vol. 14, pp. 465–471.
6. Shaykhutdinova G.F., Nikonova S.A., Karachurina R.F. Stimulating the innovation activity of business in the Republic of Bashkortostan. Vestn. UGNTU. Nauka, Obraz., Ekon. Ser. Ekon., 2016, vol. 16, no. 2, pp. 80–85. (In Russian)
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