Research of Youth Sports in the Russian Federation as a Factor of Human Capital Formation Using Neural Networks

Abstract

With the use of neural network modeling, the authors analyzed the current state of youth sports in rural areas of Russian regions, which characterizes human capital. The simulation uses neural networks implemented in the Deductor package – self-organizing Kohonen maps. As a result of the analysis, the authors obtained a distribution of regions in five clusters. The composition and characteristics of each cluster are presented. The regions with the highest indicators of sports development in rural areas have been identified. This paper shows the influence of the indicators considered on human capital, which is one of the dominant internal factors of socio-economic potential of territories. The results of the research are of practical significance for a comparative analysis of the development of children’s and youth sports in the regions of Russia and can be taken into account in the strategic planning of the development of the sports industry in the context of increasing the quality of human capital.

Research of the innovative development of the Russian Federation regions and its impact on the eco-friendliness of the economy based on neural network cluster analysis for the purpose of economic security

“…The work of Chinese scientists [11] assesses the risk of natural disasters in China using the Kohonen SOM at the regional level using 28 indicators reflecting the danger of natural disasters. This work [12] carries out neural network modeling of the activities of the regions of the Russian Federation in the field of sports and physical education. According to the results of this activity for each period under review, all the subjects of the Russian Federation were divided into 5 groups, each of which was assigned a certain rank.…”

“…It is necessary to use the clustering quality criterion in order to determine the best partition. The partitioning in the noted paper [12] is carried out without using any criterion, and the initial conditions of the process are not specified.…”

“…The method used in this article is based on the paper [12], but it has been significantly improved in terms of eliminating the indicated shortcomings. Such indicators as "learning radius at the end of the process" and "learning rate at the end of the process"varied during clustering.…”

Neurocomputing for the Research of Sustainable Regional Development of Physical Culture, Sports and Tourism in the Context of Ensuring the Quality of Human Capital

“…A particular type of neural networks, Kohonen self-organising maps (SOMs) (Kohonen, 1982;Kohonen, 1990), was chosen for the study. This type was chosen because of the following features of such neural networks (Perova and Perova, 2018;Letiagina, et al, 2020;Carboni, Russu, 2015): a) there are no model limitations when analysing multidimensional statistical data; b) SOMs do not require any external intervention in the learning process; c) their learning algorithms allow projecting multidimensional input data space with the account of topology into either two-dimensional space or into three-dimensional space. This enables visualisation of the results obtained.…”