Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network
At present, the commonly used index selection methods for macroeconomic early-warning research include K-L information volume, time difference correlation analysis, and horse farm methods. These traditional statistical methods cannot cope with the continuous changes of economic indicators, and due t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/7880652 |
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| author | Xiaoxuan Wang Jingjing Wang Ying Zhang Yixing Du |
| author_facet | Xiaoxuan Wang Jingjing Wang Ying Zhang Yixing Du |
| author_sort | Xiaoxuan Wang |
| collection | DOAJ |
| description | At present, the commonly used index selection methods for macroeconomic early-warning research include K-L information volume, time difference correlation analysis, and horse farm methods. These traditional statistical methods cannot cope with the continuous changes of economic indicators, and due to the existence of statistical errors, these methods are difficult to perform. Therefore, this paper proposes to use a self-organizing competitive neural network to select early warning indicators. Its self-learning and adaptive characteristics and fault tolerance overcome the limitations of the above statistical methods. This article proposes a method of selecting macroeconomic early-warning indicators using self-organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self-organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early-warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self-organizing competitive neural network method with the traditional KL information method. From the experimental results, compared with the KL information method, the self-organizing competitive neural network method selects more comprehensive indicators and has greater advantages in seismic resistance and stability. |
| format | Article |
| id | doaj-art-1aa4759ca45f4c48929aabefb33b02d4 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-1aa4759ca45f4c48929aabefb33b02d42025-02-03T01:07:10ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/7880652Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural NetworkXiaoxuan Wang0Jingjing Wang1Ying Zhang2Yixing Du3National School of DevelopmentHebei College of Science and TechnologyOperation OfficeInvestment Banking DepartmentAt present, the commonly used index selection methods for macroeconomic early-warning research include K-L information volume, time difference correlation analysis, and horse farm methods. These traditional statistical methods cannot cope with the continuous changes of economic indicators, and due to the existence of statistical errors, these methods are difficult to perform. Therefore, this paper proposes to use a self-organizing competitive neural network to select early warning indicators. Its self-learning and adaptive characteristics and fault tolerance overcome the limitations of the above statistical methods. This article proposes a method of selecting macroeconomic early-warning indicators using self-organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self-organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early-warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self-organizing competitive neural network method with the traditional KL information method. From the experimental results, compared with the KL information method, the self-organizing competitive neural network method selects more comprehensive indicators and has greater advantages in seismic resistance and stability.http://dx.doi.org/10.1155/2022/7880652 |
| spellingShingle | Xiaoxuan Wang Jingjing Wang Ying Zhang Yixing Du Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network Journal of Mathematics |
| title | Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network |
| title_full | Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network |
| title_fullStr | Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network |
| title_full_unstemmed | Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network |
| title_short | Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network |
| title_sort | analysis of local macroeconomic early warning model based on competitive neural network |
| url | http://dx.doi.org/10.1155/2022/7880652 |
| work_keys_str_mv | AT xiaoxuanwang analysisoflocalmacroeconomicearlywarningmodelbasedoncompetitiveneuralnetwork AT jingjingwang analysisoflocalmacroeconomicearlywarningmodelbasedoncompetitiveneuralnetwork AT yingzhang analysisoflocalmacroeconomicearlywarningmodelbasedoncompetitiveneuralnetwork AT yixingdu analysisoflocalmacroeconomicearlywarningmodelbasedoncompetitiveneuralnetwork |