RADIAL-BASIS NEURAL NETWORKS FOR ENTERPRISES ACTIVITY PREDICTION
DOI:
https://doi.org/10.30890/2709-2313.2023-17-03-012Keywords:
prediction, modeling, artificial neural networks with radial basis functions, marketing policy, performance indicators of the company.Abstract
The resulting performance of the enterprise significantly depends on the specificity of marketing policy, which is particularly important for sales-related businesses. Existing methods of enterprises activity modeling mostly based on statistics mathematicMetrics
References
Kalinichenko, M.P. (2012) Optimization models of making marketing decisions. URL: http://www.economy.nayka.com.ua/?op=1&z=1619 (Last accessed 27.02.2020).
Nikoliuk, O.M. (2012). Features application methods of mathematical modeling and estimation of enterprise competitiveness. NMU Economic Bulletin, 3, 145-154 [in Ukrainian].
Bondarchuk, O.M., Goloborodko, B. Yu. (2015). Simulation of enterprise performance indicators in the cost management system. Economic Bulletin, 1, 150-156. [in Ukrainian].
Mokhov, V.G., Stakhanov, K.S. (2016). Modelling the innovation activity of an enterprise. Bullet in of the South Ural State University. Ser. Mathematical Modelling, Programming and Computer Software, vol 9, no 2, 130-134. DOI: 10.14529 / mmp160213.
Nelles, O. (2001). Nonlinear Systems Identification. Berlin.
Radial Basis Function Network. (2017). URL: https://www.hackerearth.com/blog/developers/radial-basis-function-network (Last accessed 09. 03. 2020).
Strumiłło, P., Kamiński, W. (2002). Radial Basis Function Neural Networks: Theory and Applications. The Proceedings of the Sixth International Conference on Neural Networks and Soft Computing. Zakopane; Poland, 107-119.
Karpenko, N.V. (2016). Enterprise Marketing: Contemporary Content. Kyiv [in Ukrainian]
Dyvak, М., Maslyiak, Y., Papa, O., Savka, N. (2017). Clustering and interval analysis of heterogeneous data sample. The Proc. of 12th International Scientific and Technical Conference of “Computer Sciences and Information Technologies” (CSIT). Lviv, 528-532.
Markopoulos, A.P., Georgiopoulos, S. and Manolakos, D.E (2016). On the use of back propagation and radial basis function neural networks in surface roughness prediction. Journal of Industrial Engineering International, vol. 12, 389–400. DOI: 10.1007/s40092-016-0146-x.
Savka, N.Ya. (2011). Stages of identification of artificial neural networks with radial-basis functions. Collection of materials of the All-Ukrainian School-Seminar of Young Scientists and Students of "Advanced Computer Information Technologies" (ACIT). Ternopil, 87 [in Ukrainian].
Oliynychuk, O., Oliynychuk, R., Savka, N., Baranetska, O., Moskaliuk N., Sukhanov M. (2021). Criminological Aspects of the Unauthorized Activities within the Cyber Space. Proceedings of the 11-th International Scientific and Technical Conference “Advanced Computer Information Technologies” (ACIT’2021). Deggendorf, Germany, 514-519. DOI: 10.1109/ACIT52158.2021.9548365/. ISBN:978-1-6654-4820-8.
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