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.

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.

scholarly journals METHODOLOGICAL APPROACHES TO ASSESSING AND FORMING PROSPECTS OF INNOVATIVE DEVELOPMENT OF THE REGIONS

2021 ◽  
Vol 2 (25) ◽  
pp. 51-59
Author(s):  
A.L. Zelezinskii ◽  
◽  
O.V. Arhipova ◽  
D.V. Hodos ◽  
D.V. Parsukov ◽  
...  
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Russian Federation ◽  
Innovation Policy ◽  
Empirical Relationship ◽  
Theory And Practice ◽  
Innovation Activity ◽  
Innovative Development ◽  
The Russian Federation

The article is devoted to the study of the problems of innovative development in the regions of the Russian Federation. The aim of this work was to determine the empirical relationship between the indicators of innovation activity, economic growth and the state of the institutional environment in the regions. For this purpose, the research methodology was formed and the analysis of the subjects of the Russian Federation was carried out with the establishment of current trends in innovative, institutional and economic development. The methodological basis of the study was the grouping method, the decision tree method, as well as economic and mathematical modeling for constructing production functions. In the course of the analysis, it was found that economic growth directly depends on the innovation and institutional factors. High values of these factors allow you to get GRP per capita at the level of 510 thousand rubles and above, with low values, this indicator is 180-200 thousand rubles. The rate of economic growth is also directly proportional to these factors. It is also revealed that, first; the quality of public institutions and business institutions determines the effectiveness of development and the growth of innovation indicators at the regional level. The most significant attribute for the classification of subjects by the level of innovative development is the innovation activity index. It defines the current classification by 36.7 %, the indices of socio-economic conditions and the quality of innovation policy by 26 %. Macroeconomic modeling of economic growth in the regions depending on the level of innovative development is carried out, and the prospects for using the innovative factor as a driver of economic growth are evaluated. It is established that for a significant number of regions of the Russian Federation, the innovative way of development is not relevant in the medium term. Only for 15 territorial subjects of the Russian Federation, economic growth is real, accompanied by the development of innovative activities. Based on the results of the simulation, proposals are formulated for the directions of economic development of the regions. The article is intended for specialists and experts in the field of theory and practice of innovative development

scholarly journals Multi-level Approach to the Regulation of Clustering and Innovative Development of Russian Regions

2020 ◽  
pp. 93-96
Author(s):  
Dmitry Leonidovich Napolskikh
Keyword(s):  
Russian Federation ◽  
Theoretical Aspect ◽  
Integrated Approach ◽  
Innovative Development ◽  
Russian Regions ◽  
Cluster Policy ◽  
Logical Scheme ◽  
Level Model ◽  
The Russian Federation ◽  
Multi Level

The paper discusses the theoretical aspect of the formation of a multi-level model of clustering and innovative development of Russian regions. The purpose of the work is to identify a set of factors that affect the effectiveness and efficiency of these processes to work out an integrated approach in their regulation. The object of the study is the pecu-liarities of the functioning of cluster formations on the territory of the regions of the Russian Federa-tion. The formation and development of clusters of the entities of the Russian Federation is influenced by a combination of factors of macro- (federal dis-tricts), meso- (entities of the Russian Federation) and micro-levels (local production systems and in-dividual enterprises). Development tools for each of them are highlighted. A structural and logical scheme of a multi-level model of clustering and in-novative development of Russian regions has been developed. The functions of state authorities and local self-government within the cluster policy are defined: mediation, infrastructure, regulatory. It is summarized that the presented multi-level model has a promising potential of methodological foun-dations for managing the processes of transfor-mation of the economic space of regions, the main elements of which are the zones of clustering of production.

scholarly journals SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

2012 ◽  
Vol 18 (5) ◽  
pp. 655-661 ◽  
Author(s):  
Hadi Hasanzadehshooiili ◽  
Ali Lakirouhani ◽  
Jurgis Medzvieckas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Statistical Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Rock Bolts ◽  
Optimal Length ◽  
The Neural Network ◽  
Rock Bolting ◽  
Geological Conditions

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.

scholarly journals Social Development Forecast for Russian Regions

2018 ◽  
Vol 22 (6) ◽  
pp. 132-152
Author(s):  
L. G. Cherednichenko ◽  
R. V. Gubarev ◽  
E. I. Dzyuba ◽  
F. S. Fayzullin
Keyword(s):  
Neural Network ◽  
Social Development ◽  
Russian Federation ◽  
Objective Assessment ◽  
Russian Regions ◽  
Republic Of Bashkortostan ◽  
The Social ◽  
The Russian Federation ◽  
The Republic ◽  
Regions Of Russia

The objective of the article is to offer a proprietary technology for assessment and forecasting of social development of Russian regions. The methodological basis of the study is neural network technology (a Bayesian ensemble of dynamic neural networks of different configurations is formed) that ensure high accuracy of the forecast. The authors developed a methodology for assessing the social potential of the Russian regions. They have also designed a system of private indicators characterising the level of social development of Russian regions. The indicators have been divided into five groups: 1) population (life expectancy); 2) standard of living of the population; 3) education; 4) health care (morbidity); 5) research and innovation. The private indicators have been made comparable by normalizing their values by means of “Pattern” method. This method allows the objective assessment of the interregional “gaps” in the country across the entire system of social indicators. The social development index of the subjects of the Russian Federation has been calculated. Based on neural network technologies (Kohonen self-organizing maps) clustering of regions of Russia regarding social development has been conducted. The forecast of the social development of the Russian regions has been made. Due to the forecast, it has been established that in the leading region of the Russian Federation (Moscow) in 2017-2019 the decrease is expected in the index of social development in comparison with 2014-2016. In another leading region of the Russian Federation (St. Petersburg) the decline in comparison with 2016 is expected in the medium term. At the same time, for the Republic of Bashkortostan in 2017-2019, just a slight decrease in the level of social development is forecasted. However, it is expected that the Republic will still lag significantly behind the leading regions of Russia by social development. The example of the Republic of Bashkortostan helped to discover that the lag in social development can be explained by the “gap” in research and innovations. The authors have concluded that it is necessary to improve the effectiveness of social policy at the regional level. Thus, it is necessary not only to increase financing of the social sphere of the subjects of the Russian Federation, but also to ensure proper control of budget spending. The developed methodology can be an effective tool for forecasting and managing social development of the Russian regions by the relevant ministries and departments.

scholarly journals Tendencies of innovative development of the Russian Federation

2018 ◽  
Vol 11 (4) ◽  
pp. 338-345 ◽  
Author(s):  
L. N. Ustinova
Keyword(s):  
Russian Federation ◽  
Innovation Activity ◽  
Integrated Analysis ◽  
High Tech ◽  
Scientific Article ◽  
Scale Production ◽  
Innovative Development ◽  
Innovation Development ◽  
Innovation Activities ◽  
The Russian Federation

Today in Russia there is a definite situation in the field of innovative development, characterized by a number of distinctive features. In a dynamic environment of industrial production and entrepreneurship, the need for the formation and development of non-proprietary resources is beyond doubt. The dynamics of productivity growth, the development of new high-tech production systems, the growth of the patent base in enterprises, trends in innovative development in general in the sector of the economy and large-scale production.The state of development of the economic system, depending on the structure of the innovation development of the subjects. At the same time, the innovative development of companies is manifested both in investments in advanced production technologies and in the production of high-tech products. The purpose of the scientific article is to analyze the innovative development in the Russian Federation.The goal necessitated the following tasks:1. To analyze the indirect indicator of innovation development – the volume of domestic expenditures on research and development in the context of country adaptability;2. To analyze the innovation activity of organizations by districts of the Russian Federation;3. To produce a factor analysis of indicators on the innovative development of the Russian Federation;4. Formulate recommendations for improving innovation activities in Russia.The article used modern methods and tools for integrated analysis based on the systematization and structuring of thematic material. So, in the article in which you will find information about what is happening in Russia.The above studies have become conclusions and recommendations regarding the features of the innovative development of the Russian Federation, recommendations for improving innovation activities in the Russian Federation.Significance is expressed in the practical possibility of using the results of research results. 

scholarly journals The international clusters in the innovative development of an energy efficient economy

2019 ◽  
Vol 91 ◽  
pp. 08031 ◽  
Author(s):  
Inga Skvortsova ◽  
Roman Latyshev ◽  
Kirill Grabovyy
Keyword(s):  
Russian Federation ◽  
Energy Efficient ◽  
New Technologies ◽  
Current Level ◽  
Innovation Activity ◽  
Innovative Development ◽  
Efficient Management ◽  
The Russian Federation ◽  
Innovative Potential

The purpose of this article is to analyze the current level of innovation activity in the Russian Federation, to determine the role of international clusters in the innovative development of the energy efficient country’s economy, while focusing on the formation of the innovative potential of international cooperation of cluster members. The urgency of the creation of cluster associations - international consortia. Examples of the joint implementation of innovative projects in the field of energy-efficient management and the application of new technologies in the field of sustainable energy for megacities are given.

Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions

2010 ◽  
Vol 132 (7) ◽  
Author(s):  
Ling-Xiao Zhao ◽  
Liang Yang ◽  
Chun-Lu Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Modeling ◽  
Cooling Capacity ◽  
Neural Network Modeling ◽  
Pressure Drops ◽  
Data Errors ◽  
Single Output ◽  
Dry And Wet Conditions ◽  
And Training

A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%.

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