SHS Web Conf.
Volume 73, 2020Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
|Number of page(s)||12|
|Section||Potential of the Eurasian Economic Union|
|Published online||13 January 2020|
- D. Chicea, S.M. Rei, A fast artificial neural network approach for dynamic light scattering time series processing. Measurement Science and Technology, 29(10) (2018) [CrossRef] [Google Scholar]
- M.S. Hossain, Z.C. Ong, Z. Ismail, S. Noroozi, S.Y. Khoo, Artificial neural networks for vibration based inverse parametric identifications: A review. Applied Soft Computing, 52, 203-219 (2017) [CrossRef] [Google Scholar]
- M.V. Cho. Learning Process in a Neural Network Model. Journal of the Korean Physical Society, 74(1), 63-72 (2019) [CrossRef] [Google Scholar]
- M. Vochozka, Formation of complex company evaluation method through neural networks based on the example of construction companies´ collection. AD ALTA – Journal of Interdisciplinary Research, 7(2), 232-23 (2017) [Google Scholar]
- Q.J. Zhu, L.C. Tian, X.H. Yang, L.F. Gan, N. Zhao, Y.Y. Ma, Advantages of Artificial Neural Network in Neutron Spectra Unfolding. Chinese Physics Letters, 31(7) (2014) [Google Scholar]
- Z. Rowland, J. Vrbka, Using artificial neural networks for prediction of key indicators of a company in global world. 16th International Scientific Conference on Globalization and its Socio-Economic Consequences, pp.1896-1903. ISBN 978-80-8154-191-9 (2016) [Google Scholar]
- D. Sánchez, P. Melin, Modular Neural Networks for Time Series Prediction Using Type-1 Fuzzy Logic Integration. Studies in Computational Intelligence: Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization, 601, 141-145 (2015) [Google Scholar]
- M. Deng, W.T. Yang, G.L. Liu, R. Jin, F. Xu, Y. Zhang, Heterogeneous Space-Time Artificial Neural Networks for Space-Time Series Prediction. Transactions in GIS, 22(1), 183-201 (2018) [CrossRef] [Google Scholar]
- M.K. Rafsanjani, M. Samareh, Chaotictimeseriesprediction by artificial neural network. Journal of Computational Methods in Sciences and Engineering, 16(3), 599-615 (2016) [CrossRef] [Google Scholar]
- B. Wang, S.H. Xu, X.H. Yu, P.C. Li, Time Series Forecasting Based on Cloud Process Neural Network. International Journal of Computational Intelligence Systems, 8(5) 992-1003 (2015) [CrossRef] [Google Scholar]
- F. Fernandez-Navarro, M.A. de la Cruz, P.A. Gutierrez, A. Castano, C. Hervas-Martinez, Time series forecasting by recurrent product unit neural network. Neural Computing & Applications, 29(3), 779-791 (2017) [CrossRef] [Google Scholar]
- T. Klieštik, J. Vrbka, Z. Rowland, Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium – Quarterly Journal of Economics and Economic Policy, 13(3), 569-593 (2018) [Google Scholar]
- Ministry of Industry and Trade – MIT, Export v ekonomice [Export in economy] [online], Available at: https://www.mpo.cz/ (2018) [Google Scholar]
- J. Gourdon, S. Monjon, S. Poncet, Trade policy and industrial policy in China: What motivates public authorities to apply restrictions on exports? China Economic Review, 40, 105-120 (2016) [CrossRef] [Google Scholar]
- Business info.cz., China: Trade and Economic Cooperation with the Czech Republic, [online],Available at: https://www.businessinfo.cz/cs/clanky/cina-obchodni-a-ekonomicka-spoluprace-s-cr-19054.html (2018) [Google Scholar]
- V. Stehel, P. Šuleř, Foreign trade between China and the Czech Republic. Littera Scripta, 9(3), 84-95 (2016) [Google Scholar]
- V. Humlerová, Czech-Chinese Business Cooperation Case Study. 31st International-Business-Information-Management-Association Conference Innovation management and education excellence through vision 2020, pp. 3185-3191 (2018) [Google Scholar]
- P. Higgins, T. Tha, W. Zhong, Forecasting China’s economic growth and inflation. China Economic Review, 41, 46-61 (2016) [CrossRef] [Google Scholar]
- T. Klieštik, M. Mišánková, K. Valášková, L. Švábová, Bankruptcy prevention: new effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2), 791-803 (2018) [Google Scholar]
- Ch. Bolton, V. Machová, M. Kováčová, K. Valášková, The power of human-machine collaboration: Artificial intelligence, business automation, and the smart economy. Economics, Management, and Financial Markets, 13(4), 51-56 (2018) [CrossRef] [Google Scholar]
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