scholarly journals Neural network modeling of development trends of physical culture and sports in the Russian regions as a driver of the national socio-economic growth

2018 ◽  
Vol 14 (11) ◽  
pp. 2064-2082
Author(s):  
V.I. Perova ◽  
◽  
N.A. Perova ◽  
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Physical Culture ◽  
Russian Regions ◽  
Development Trends

Studying the innovative development of regional economy as an imperative of sustainable socio-economic growth in Russia, using neural network modeling

2021 ◽  
Vol 20 (8) ◽  
pp. 1394-1414
Author(s):  
Nikolai P. LYUBUSHIN ◽  
Elena N. LETYAGINA ◽  
Valentina I. PEROVA
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Neural Networks ◽  
Russian Federation ◽  
Network Modeling ◽  
Innovation Activity ◽  
Neural Network Modeling ◽  
Innovative Development ◽  
Russian Regions ◽  
The Russian Federation

Subject. The article deals with the innovative potential of Russian regions in light of the national goal of the Russian Federation development, reflecting decent and productive work. Objectives. The purpose is to study the innovation activity in Russian regions, using neural networks, to ensure breakthrough innovative development of the Russian economy. Methods. We employ a cluster analysis on the basis of neural network modeling, using information technologies. For the research, we selected neural networks (Kohonen self-organizing maps), which are focused on unsupervised learning and are a promising tool for clustering and visualization of multidimensional statistical data. Results. The result of neural network modeling was the ranking of 85 regions of the Russian Federation into 5 compact groups (clusters) regardless of their affiliation to federal districts of the Russian Federation. The study shows that there is a strong differentiation of the number of regions in these clusters. We obtained average values of indicators in the clusters and compared them with all-Russian indicators. Conclusions. Breakthrough in the socio-economic growth of the Russian Federation is associated with a set of measures that involve stimulating innovation activities in regions, which are characterized by different level of innovation development. Such measures will increase the interest of the real sector of the economy in using scientific development, advanced production technologies, higher-productivity employment opportunities, and, as a result, will encourage socio-economic growth and people's quality of life.

Neural network modeling of innovative potential of Russian regions

2020 ◽  
Author(s):  
E. N. Letiagina ◽  
V. I. Perova ◽  
D. N. Malov
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Russian Regions ◽  
Innovative Potential

Проведено нейросетевое моделирование современного состояния инновационного потенциала регионов Российской Федерации на основе искусственных нейронных сетей — самоорганизующихся карт Кохонена с использованием информационных технологий. Кластерный анализ на основе нейронных сетей показал неравномерность инновационного развития регионов России. Предложен методический подход к формированию инновационных кластеров.

scholarly journals Neural network modeling of tourism development as a factor of sustainable economic growth in Russian regions

2021 ◽  
Vol 291 ◽  
pp. 02023
Author(s):  
Elena Letiagina ◽  
Valentina Perova ◽  
Aleksander Gutko ◽  
Aleksander Kutasin
Keyword(s):  
Neural Network ◽  
Russian Federation ◽  
Economic Activity ◽  
Dimensional Space ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Russian Regions ◽  
Tourism Sector ◽  
The Russian Federation ◽  
Regions Of Russia

The features of the development of the tourism sector in the regions of the Russian Federation, which have an impact on the socio-economic development of the country, have been investigated. Analysis of the current state of the tourism sector, classified as the main types of economic activity, is relevant and important for increasing the competitiveness of the regions of the Russian Federation and ensuring the economic security of the state. The study is aimed to model and analyze tourist cluster formations in Russia. The study of tourist activity in the regions of Russia based on the indicators of the database of the Federal State Statistics Service was carried out using a new promising approach - cluster analysis using the scientific and methodological apparatus of artificial neural networks. The distribution of Russian regions into five tourist clusters has been obtained as a result of clustering multidimensional data using neural networks - self-organizing Kohonen maps, which are focused on self-study, and modern information technologies. In neural network modeling, the six-dimensional space of tourism development indicators was mapped, taking into account the topology, into a two-dimensional space, which made it possible to visualize the results of grouping regions by tourist clusters. The features of the development of the tourism sector in the regions of the Russian Federation have been revealed by the totality of the considered indicators The obtained results state that there is a strong variation in the number of regions by tourist clusters and the ametric nature of the development of tourist activity in the regions of Russia. The results of the study are of practical significance for the strategic planning of the tourism sector development, which ensures the development of domestic and inbound tourism. Analysis of the functioning of the tourism sector in the regions of the Russian Federation allows concluding the necessity to take a set of measures to stimulate effective investment activity in a number of tourism clusters, harmonizing the strategies of the state and business, which will contribute to the renewal and competitiveness of this type of economic activity.

Water quality parameter forecast based on grey neural network modeling

2009 ◽  
Vol 29 (6) ◽  
pp. 1529-1531 ◽  
Author(s):  
Wei-ren SHI ◽  
Yan-xia WANG ◽  
Yun-jian TANG ◽  
Min FAN
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Network Modeling ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Neural Network Modeling

ε-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling

2012 ◽  
Vol 34 (6) ◽  
pp. 1414-1419
Author(s):  
Qing-bing Sang ◽  
Zhao-hong Deng ◽  
Shi-tong Wang ◽  
Xiao-jun Wu
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Network Modeling ◽  
Neural Network Modeling
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