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

The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic... AbstractThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Mathematics, Statistics and Informatics de Gruyter

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

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Publisher
de Gruyter
Copyright
© 2022 X. Gui et al., published by Sciendo
ISSN
1336-9180
eISSN
1339-0015
DOI
10.2478/jamsi-2022-0011
Publisher site
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Abstract

AbstractThis 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.

Journal

Journal of Applied Mathematics, Statistics and Informatics – de Gruyter

Published: Dec 1, 2022

Keywords: Prediction; Gross domestic product; Genetic algorithm back-propagation combined model; Particle swarm optimization back-propagation combined model; 26A33; 26A51; 26D15

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