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Comparative analysis of types of neural networks for solving problems of modeling socioeconomic systems (forecasting of production using neural networks, for example, on an LSTM-type network) Cover

Comparative analysis of types of neural networks for solving problems of modeling socioeconomic systems (forecasting of production using neural networks, for example, on an LSTM-type network)

Open Access
|Mar 2026

Figures & Tables

Figure 1:

GRP dynamics by federal districts from 2014 to 2020.

Figure 1:

RBFNs. RBFNs, radial basis function networks.

Figure 2:

CNNs. CNNs, convolutional neural networks.

Figure 3:

LSTM networks. LSTM, long short-term memory.

Figure 4:

RNNs. RNNs, recurrent neural networks.

Figure 5:

GANs. GANs, generative adversarial networks.

Figure 6:

MLPs. MLPs, multilayer perceptrons.

Figure 7:

SOMs. SOMs, self-organizing maps.

Figure 8:

Belief networks (DBNs). RBM, restricted Boltzmann machine.

Figure 9:

RBMs. RBMs, restricted Boltzmann machines.

Figure 10:

Autoencoders.

Figure 11:

Casing coupling product output forecast.

Figure 12:

Production forecast tubing coupling 73 mm.
Language: English
Submitted on: Apr 4, 2024
Published on: Mar 15, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Tatiana A. Makarenya, Ali Sajae Mannaa, Alexey I. Kalinichenko, Svetlana V. Petrenko, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.