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Economic issue of using artificial neural networks with radial-basis transmission functions for modeling efficiency of management processes
1
WSEI University, Poland
2
Lublin University of Technology, Poland
3
University of Rzeszow
Data nadesłania: 27-11-2023
Data akceptacji: 06-12-2023
Data publikacji: 18-12-2023
JoMS 2023;54(Numer specjalny 5):410-426
SŁOWA KLUCZOWE
artificial neural networks with radial-basis transmission functionscustoms systemindicators of economic security of the statemacroeconomic forecastingeconomic security of the state
DZIEDZINY
STRESZCZENIE
Objectives:
The paper presents the possibility of using artificial neural networks (ANN) with radial-basis transmission function (RBF) for modeling of economic phenomena and processes.
Material and methods:
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.
Results:
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.
Conclusions:
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.
The paper presents the possibility of using artificial neural networks (ANN) with radial-basis transmission function (RBF) for modeling of economic phenomena and processes.
Material and methods:
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.
Results:
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.
Conclusions:
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.
Licencja
REFERENCJE (21)
1.
Aminian, F., Suarez, E. D., Aminian, M., & Walz, D. T. (2006). Forecasting economic data with neural networks. Computational Economics, No.28, 71-88.
2.
Babkin, A. V., Karlina, E. P., & Epifanova, N. S. (2015). Neural networks as a tool of forecasting of socioeconomic systems strategic development. Procedia-Social and Behavioral Sciences, No.207, 274-279.
3.
Bodiansky, E., Rudenko, O. (2004). Artificial neural networks: architectures, training, applications. TELETECH, Kharkov.
4.
Dyvak, M., Pukas, A., Kozak, O. (2008). Tolerance estimation of parameters set of models created on experimental data. International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science.
5.
Isik, F., & Ozden, G. (2012). Estimating compaction parameters of fine – and coarse-grained soils by means of artificial neural networks. Environmental Earth Sciences, 69(7), 2287–2297. https://doi.org/10.1007/s12665....
6.
Kovel, J.(2007). The enemy of nature: The end of capitalism or the end of the world? Zed Books, London.
7.
Krausmann, F., Erb, K.-H., Gingrich, S., Haberl, H., Bondeau, A., Gaube, V., Lauk, C., Plutzar, C., & Searchinger, T. D.(2013). Global human appropriation of net primary production doubled in the 20th century. Proceedings of the National Academy of Sciences, 110(25), 10324–10329. https://doi.org/10.1073/pnas.1....
8.
Kruglov, V., Borisov, V. (2001). Artificial neural networks. Theory and Practice. Telecom, Moscow.
9.
Liu, C., & Peng, A. 2010. A reinvestigation of contract duration using Quantile Regression for Counts analysis. Economics Letters, 106(3), 184–187. https://doi.org/10.1016/j.econ....
10.
Martyniuk, V. (2020). Economic security as a basis for cross-border cooperation of Poland and Ukraine. Security in transborder regional cooperation:Ukraine vs Poland . Edited by Olena Kovalchuk Lublin, 2020. 89-101.
11.
Martyniuk, V., Muravska, Y. (2020). Forming a foreign trade partnership strategy in the context of strengthening national economic security: A case study of Ukraine. Forum Scientiae Oeconomia. Vol 8 No 2. 5-24.
12.
Ministry for Development of Economy, Trade and Agriculture of Ukraine Homepage, https://www.me.gov.ua, last accessed 2020/02/20.
13.
Nelles, O. (2001). Nonlinear System Identification: from Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin.
14.
Rudenko, O., Bodiansky, E. (2006). Artificial neural networks. SMIT Company LLC, Kharkiv.
15.
State Statistics Service of Ukraine Homepage, http://www.ukrstat.gov.ua, last accessed 2019/10/07.
16.
Tao, K. M. (1993). A closer look at the radial basis function (RBF) networks. In Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, 401-405.
17.
Wołowiec, T., Suseł, A. (2010). Conception of the optimal and safe strategy of material reserves management on the basis of the probabilistic model. Internet, Competititiveness and Organizational Security. Żlin: Tomas Bata University in Zlin and European Association for Security.
18.
Wołowiec, T., Szybowski, S., Prokopowicz, D. (2019). Methods of Development Network Analysis as a Tool Improving Efficient Organization Management, International Journal of New Economics and Social Sciences № 1 (9),34-42.
19.
Xiong, Q., Yong, S., Shi, W., Chen, J., & Liang, Y. (2005). The research of forecasting model based on RBF neural network. In 2005 International Conference on Neural Networks and Brain Vol. 2, 1032-1035.
20.
Yasnitsky, L. (2008). Introduction to Artificial Intelligence. Akademiya Publishing Center, Moscow.
21.
Zhuravka, F., Filatova, H., Šuleř, P., Wołowiec, T. (2020). State debt assessment and forecasting: time series analysis. Investment Management and Financial Innovations, 18(1), 65-75.
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| eISSN: | 2391-789X |
| ISSN: | 1734-2031 |
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