<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper adopts different optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO-Algorithm) to train Back-Propagation (BP) neural networks, fits the Chinese, the Czech, Slovak, Hungarian, and Polish gross domestic product (GDP) growth model (from 1995 to 2020) and makes short-term simulation predictions. We use the PSO-Algorithm and GA with strong global search ability to optimize the weights and thresholds of the network, combine them with the BP neural network, and apply the resulting Particle Swarm Optimization Back-Propagation (PSO-BP) combined model or Genetic-Algorithm Back-Propagation (GA-BP) combined model to allow the network to achieve fast convergence. Besides, we also compare the above two hybrid models with standard multivariate regression model and BP neural network with different initialization methods like normal uniform and Xavier for fitting and short-term simulation predictions. Finally, we obtain the excellent results that all the above models have achieved a good fitting effect and PSO-BP combined model on the whole has a smaller error than others in predicting GDP values. Through the technology of PSO-BP and GA-BP, we have a clearer understanding of the five countries gross domestic product growth trends, which is conducive to the government to make reasonable decisions on the economic development.</p>
</abstract>

This paper adopts different optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO-Algorithm) to train Back-Propagation (BP) neural networks, fits the Chinese, the Czech, Slovak, Hungarian, and Polish gross domestic product (GDP) growth model (from 1995 to 2020) and makes short-term simulation predictions. We use the PSO-Algorithm and GA with strong global search ability to optimize the weights and thresholds of the network, combine them with the BP neural network, and apply the resulting Particle Swarm Optimization Back-Propagation (PSO-BP) combined model or Genetic-Algorithm Back-Propagation (GA-BP) combined model to allow the network to achieve fast convergence. Besides, we also compare the above two hybrid models with standard multivariate regression model and BP neural network with different initialization methods like normal uniform and Xavier for fitting and short-term simulation predictions. Finally, we obtain the excellent results that all the above models have achieved a good fitting effect and PSO-BP combined model on the whole has a smaller error than others in predicting GDP values. Through the technology of PSO-BP and GA-BP, we have a clearer understanding of the five countries gross domestic product growth trends, which is conducive to the government to make reasonable decisions on the economic development.

The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model

Department of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics, Physics and Informatics

Journal of Applied Mathematics, Statistics and Informatics

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper adopts different optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO-Algorithm) to train...

The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model

Pubblicato online: 19 gen 2023

Pagine: 33 - 56

DOI: https://doi.org/10.2478/jamsi-2022-0011

Parole chiave
Prediction, Gross domestic product, Genetic algorithm back-propagation combined model, Particle swarm optimization back-propagation combined model

© 2022 X. Gui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model

Pubblicato online: 19 gen 2023

Pagine: 33 - 56

DOI: https://doi.org/10.2478/jamsi-2022-0011

Parole chiavePrediction, Gross domestic product, Genetic algorithm back-propagation combined model, Particle swarm optimization back-propagation combined model

© 2022 X. Gui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Prediction, Gross domestic product, Genetic algorithm back-propagation combined model, Particle swarm optimization back-propagation combined model