Open AccessNeural network modelling for determining the priority areas of regional development
Author(s) -
A. K. Moskalev,
A. E. Petrunina,
Nikita Tsygankov
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/986/1/012017
Subject(s) - self organizing map , artificial neural network , cluster analysis , order (exchange) , computer science , cluster (spacecraft) , process (computing) , key (lock) , business cluster , artificial intelligence , business , industrial organization , philosophy , computer security , finance , epistemology , mechanism (biology) , programming language , operating system
Neural network modeling based on self-organizing Kohonen maps was carried out in order to cluster the economy of one of the administrative districts of the Krasnoyarsk Territory. The research is based on statistical data on the economy of about one hundred enterprises of various spheres operating in the region. It is shown that in the process of clustering four groups of enterprises can be clearly distinguished according to the types of economic activity. Having applied the neural network modelling method, we identified three enterprises within the framework of this cluster, which can be considered as “growth areas” of the district economy. Self-organizing maps that is trained using unsupervised learning make it possible to get an idea not only of promising areas of development, but also to identify key parameters that ensure the leadership and competitiveness of the region.