Economic Analysis: Theory and Practice
 

Neural simulation of behavior pattern of Russian agriculture development

Vol. 17, Iss. 2, FEBRUARY 2018

Received: 14 April 2017

Received in revised form: 15 November 2017

Accepted: 12 December 2017

Available online: 1 March 2018

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: 

Pages: 379–396

https://doi.org/10.24891/ea.17.2.379

Perova V.I. National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation
perova_vi@mail.ru

ORCID id: not available

Korchemnyi P.V. National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation
mmep@iee.unn.ru

ORCID id: not available

Importance The article addresses the specifics of behavior pattern of agriculture development in the Russian Federation. An analysis of the current status of agriculture, which is classified as a priority economic sector, is important for improving the country's food security.
Objectives The purpose of the study is to investigate changes in agriculture in the regions of the Russian Federation through neural simulation and analyze the data of the Federal State Statistics Service on the status of the sector.
Methods We analyze indicators that describe the dynamics of agroindustrial complex in the regions of the Russian Federation for 2010–2014, using the self organizing maps based on the Deductor package.
Results Based on trends in agricultural development indicators, we grouped Russian regions into four clusters. For 2010–2014, cluster nuclei with permanent structure of regions were formed in two clusters, i.e. a cluster with high values of indicators under consideration and a cluster with average values throughout Russia, toward which most of regions gravitate.
Conclusions The obtained results are of practical relevance for strategic planning in agriculture. The analysis of trends in agriculture enables to conclude that a set of measures should be adopted to stimulate scientific and technological progress, effective innovation and investment, which will contribute to AIC renewal and competitiveness.

Keywords: growth dynamics, cluster analysis, neural networks, Kohonen self-organizing map, Deductor

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ISSN 2311-8725 (Online)
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