[
R. E. Lucas, On the mechanics of economic development, Journal of Monetary Economics, 22 (1988), 3C42.
]Search in Google Scholar
[
W.J. Baumol, et al, Convergence of Productivity, Oxford University Press, 1994, 20C61.
]Search in Google Scholar
[
D. Luo, J. Wang, M. Fečkan, Applying fractional calculus to analyze economic growth modelling, Mathematics, 14(2018), 25-36.
]Search in Google Scholar
[
H. Ming, J. Wang, M. Fečkan, The application of fractional calculus in Chinese economic growth models, Mathematics, 7(2019), 665.
]Search in Google Scholar
[
M. Ilie, N. Popovici, C. Ilie, Simulation with artificial intelligence to forecast gdp depending on logistics elements, International Management Conference, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, 2015.
]Search in Google Scholar
[
Z. Xiao, S. Ye, B. Zhong, et al, BP neural network with rough set for short term load forecasting, Applied Artificial Intelligence Review, 36(2009), 273-279.
]Search in Google Scholar
[
L. Feng, J. Zhang, Application of artificial neural networks in tendency forecasting of economic growth, American Economic Journal Economic Policy, 40(2014),76-80.
]Search in Google Scholar
[
X. Wang, J. Wang, M. Fečkan, BP neural network calculus in economic growth modelling of the group of seven, Mathematics, 8(2020), 11.
]Search in Google Scholar
[
C. Chuku, A. Simpasa, J. Oduor, Intelligent forecasting of economic growth for developing economies, International Economics, 159(2019), 74-93.
]Search in Google Scholar
[
S. Mladenović, M. Milovančević, I. Mladenović, et al, Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters, Computers in Human Behavior, 65(2016), 43-45.
]Search in Google Scholar
[
L. Feng, J. Zhang, Application of artificial neural networks in tendency forecasting of economic growth, Economic Modelling, 40(2014), 76-80.
]Search in Google Scholar
[
C. Wang, Y. Cao, Forecasting Chinese economic growth, energy consumption, and urbanization using two novel grey multivariable forecasting models, Journal of Cleaner Production, 299(2021), 126863.
]Search in Google Scholar
[
R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory, MHS’95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, 2002.
]Search in Google Scholar
[
K. Y. Lee, M. A. El-Sharkawi, Modern heuristic optimization techniques, Fundamentals of Particle Swarm Optimization Techniques, 2008, 71-87.
]Search in Google Scholar
[
M. Alba, R. Fernando, Evaluating genetic algorithms through the approximability hierarchy, Journal of Computational Science, 2021, 53, 101388.
]Search in Google Scholar
[
J. Holland, Adaptation in natural and artificial systems : an introductory analysis with application to biology, Control & Artificial Intelligence, 1975.
]Search in Google Scholar
[
S. Ethaib, R. Omar, M. K. S. Mazlina, et al, Development of a hybrid PSOCANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass, NEURAL COMPUTING & APPLICATIONS, 2016.
]Search in Google Scholar
[
Y. Mei, J. Yang, Y. Lu, et al, BP-ANN model coupled with particle swarm optimization for the efficient prediction of 2-chlorophenol removal in an electro-oxidation system, International Journal of Environmental Research and Public Health, 16(2019), 2454.
]Search in Google Scholar
[
F. van-den-Bergh, A. P. Engelbrecht, Using cooperative particle swarm optimization to train product unit neural networks, IEEE International Joint Conference on Neural Networks, Washington D C, USA, 2001.
]Search in Google Scholar
[
E. Assareh, M. A. Behrang, M. R. Assari, et al, Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran, Energy, 35(2010), 5223-5229.
]Search in Google Scholar
[
K. Harun, C. Halim, H. Arif, et al, Estimating petroleum exergy production and consumption using vehicle ownership and GDP based on genetic algorithm approach, Renewable and Sustainable Energy Reviews, 8(2004), 289-302.
]Search in Google Scholar
[
J. Nishtha, S. Bharti, Particle Swarm and Genetic Algorithm applied to mutation testing for test data generation: A comparative evaluation, Journal of King Saud University - Computer and Information Sciences, 32(2020), 514-521.
]Search in Google Scholar
[
X. Wang, Introduction to neural networks, Science Press, Beijing, China, 12(2016), 1-108. (Chinese)
]Search in Google Scholar
[
I. C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, 85(2003), 317-325.
]Search in Google Scholar
[
Y. Lyu, J. Nie, S. X. Yang, Forecasting US economic growth in downturns using cross-country data, Economics Letters, 198(2021), 109668.
]Search in Google Scholar
