A comparative study of different neural networks in predicting gross domestic product
Gross domestic product (GDP) can well reflect the development of the economy, and predicting GDP can help better grasp the future economic trends. In this article, three different neural network models, the genetic algorithm – back-propagation neural network model, the particle swarm optimization (P...
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-05-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2022-0042 |
| _version_ | 1811236547252453376 |
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| author | Lai Han |
| author_facet | Lai Han |
| author_sort | Lai Han |
| collection | DOAJ |
| description | Gross domestic product (GDP) can well reflect the development of the economy, and predicting GDP can help better grasp the future economic trends. In this article, three different neural network models, the genetic algorithm – back-propagation neural network model, the particle swarm optimization (PSO) – Elman neural network (Elman NN) model, and the bat algorithm – long short-term memory model, were analyzed based on neural networks. The GDP data of Sichuan province from 1992 to 2020 were collected to compare the performance of the three models in predicting GDP. It was found that the mean absolute percentage error values of the three models were 0.0578, 0.0236, and 0.0654, respectively; the root-mean-square error values were 0.0287, 0.0166, and 0.0465, respectively; and the PSO-Elman NN model had the best performance in GDP prediction. The experimental results demonstrate that neural networks were reliable in predicting GDP and can be used for further applications in practice. |
| first_indexed | 2024-04-12T12:10:04Z |
| format | Article |
| id | doaj.art-5621a906f0a74f26a291d3d4fb5b123c |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-12T12:10:04Z |
| publishDate | 2022-05-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-5621a906f0a74f26a291d3d4fb5b123c2022-12-22T03:33:35ZengDe GruyterJournal of Intelligent Systems2191-026X2022-05-0131160161010.1515/jisys-2022-0042A comparative study of different neural networks in predicting gross domestic productLai Han0Sichuan TOP IT Vocational Institute, No. 2000, West District Avenue, High-Tech West District, Chengdu, Sichuan 611743, ChinaGross domestic product (GDP) can well reflect the development of the economy, and predicting GDP can help better grasp the future economic trends. In this article, three different neural network models, the genetic algorithm – back-propagation neural network model, the particle swarm optimization (PSO) – Elman neural network (Elman NN) model, and the bat algorithm – long short-term memory model, were analyzed based on neural networks. The GDP data of Sichuan province from 1992 to 2020 were collected to compare the performance of the three models in predicting GDP. It was found that the mean absolute percentage error values of the three models were 0.0578, 0.0236, and 0.0654, respectively; the root-mean-square error values were 0.0287, 0.0166, and 0.0465, respectively; and the PSO-Elman NN model had the best performance in GDP prediction. The experimental results demonstrate that neural networks were reliable in predicting GDP and can be used for further applications in practice.https://doi.org/10.1515/jisys-2022-0042neural networksichuan provincegross domestic productoptimization algorithmforecasting |
| spellingShingle | Lai Han A comparative study of different neural networks in predicting gross domestic product Journal of Intelligent Systems neural network sichuan province gross domestic product optimization algorithm forecasting |
| title | A comparative study of different neural networks in predicting gross domestic product |
| title_full | A comparative study of different neural networks in predicting gross domestic product |
| title_fullStr | A comparative study of different neural networks in predicting gross domestic product |
| title_full_unstemmed | A comparative study of different neural networks in predicting gross domestic product |
| title_short | A comparative study of different neural networks in predicting gross domestic product |
| title_sort | comparative study of different neural networks in predicting gross domestic product |
| topic | neural network sichuan province gross domestic product optimization algorithm forecasting |
| url | https://doi.org/10.1515/jisys-2022-0042 |
| work_keys_str_mv | AT laihan acomparativestudyofdifferentneuralnetworksinpredictinggrossdomesticproduct AT laihan comparativestudyofdifferentneuralnetworksinpredictinggrossdomesticproduct |