Economic issue of using artificial neural networks with radial-basis transmission functions for modeling efficiency of management processes
Ładowanie...
Data
2023
Inny tytuł
Typ
Artykuł recenzyjny
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Ekonomia i finanse
Czasopismo lub seria
Journal of Modern Science
ISSN
1734-2031
2391-789X
2391-789X
ISBN
DOI
10.13166/jms/176383
Strona internetowa
Wydawca
Wydawca
Wydanie
Numer
Strony od-do
Tytuł monografii
item.page.defence
Tytuł tomu
Opis
Rodzaj licencji
Abstrakt (en)
The modelling of economic phenomena has for many years been in the centre of interest of researchers, both from the perspective of economic sciences and from the point of view of management processes. The development of such models makes it possible, on the one hand, to forecast the development of specific phenomena, but also to assess the effects of various economic decisions, including the evaluation of their social consequences, the identification of potential risks, or the optimisation of resource utilisation. On the other hand, in a great many cases classical statistical methods, such as regression analysis or structural equation modelling, did not make it possible to identify reliable and credible models linking current events in the economy and their potential effects. The consequence of the multiplicity of possible influencing factors, the various, usually non-linear mechanisms of their influence, or their multidimensional interactions, was to obtain models with limited fit, which did not reflect the nature of the relationship under study very well, nor did they have much predictive power. Artificial neural networks (ANN) are a solution for generating models with bet ter properties, capable of modelling complex relationships, taking into account the different nature of the data, as well as having clear capabilities for predicting future states. The article evaluates the possibility of applying an approach based on artificial neural networks using a radial basis function (RBF) as an activation function to the construction of an exemplary economic model representing the relationship between the efficiency of the customs system and the economic security of the state, in this case Ukraine, developed on official statistical data. The analysis carried out proved that a model based on an artificial neural network makes it possible to accurately predict the development of the economic phenomenon under study. This makes it possible, on the one hand, to simulate its reaction to chang ing environmental conditions and, on the other, to potentially assess the significance of the impact of individual input variables on its shape and intensity. The results obtained therefore clearly indicate the possibility of using artificial neural networks to build economic models, and are also a premise for replacing classical modelling methods with methods based on such networks.