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Economic issue of using artificial neural networks with radial-basis transmission functions for modeling efficiency of management processes

cris.lastimport.scopus2024-09-19T01:30:10Z
dc.abstract.enThe 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.
dc.affiliationAdministracji i Nauk Społecznych
dc.contributor.authorVolodymyr Martynyuk
dc.contributor.authorArtur Dmowski
dc.contributor.authorMarcin Gąsior
dc.contributor.authorGrzegorz Hajduk
dc.date.accessioned2024-03-22T11:55:40Z
dc.date.available2024-03-22T11:55:40Z
dc.date.issued2023
dc.description.abstract<jats:sec><jats:title>Objectives</jats:title><jats:p>The paper presents the possibility of using artificial neural networks (ANN) with radial-basis transmission function (RBF) for modeling of economic phenomena and processes.</jats:p></jats:sec><jats:sec><jats:title>Material and methods</jats:title><jats:p>The basic characteristics and parameters of an ANN with RBF are shown and the advantages of using this type of ANN for modeling economic phenomena and processes are emphasized. Using an ANN with RBF, together with official statistics for 2010-2017, the modeling of the influence caused by work efficiency indicators of the customs authorities of Ukraine on the indicators of economic security of Ukraine was carried out. These eighteen indicators of economic security of Ukraine, which comprehensively characterize the economic status of the country in terms of production, social, financial, food, transport, energy, and foreign economic security, were chosen as the most informative indicators.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The results of the study showed that Artificial neural networks with Radial-basis transmission function well describe the trend of changing state economic security indicators under the influence of changing performance indicators of customs authorities. This allows us to recommend this type of artificial neural networks for analysis, evaluation and forecasting of economic phenomena and processes.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The results obtained showed good analytical and prognostic properties of an ANN with RBF when modeling the impact of customs authorities' performance on the state's economic security indicators.</jats:p></jats:sec>
dc.identifier.doi10.13166/jms/176383
dc.identifier.issn1734-2031
dc.identifier.issn2391-789X
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/134
dc.pbn.affiliationeconomics and finance
dc.relation.ispartofJournal of Modern Science
dc.rightsCC-BY-SA
dc.subject.enartificial neural networks with radial-basis transmission functions
dc.subject.enindicators of economic security of the state
dc.subject.enmacroeconomic forecasting
dc.subject.eneconomic security of the state
dc.subject.encustoms system
dc.titleEconomic issue of using artificial neural networks with radial-basis transmission functions for modeling efficiency of management processes
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue5
oaire.citation.volume54