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- W4328096138 abstract "Guizhou Province, as an inland province of China without borders and seas, has relatively backward economic development, but its economic growth is fast and its development potential is great. Studying its import and export trade is important to improve residents ' income and accelerate internationalization. This paper analyzes and establishes the indicator system of regional import and export influencing factors through literature analysis, and then uses ( radial basis function ) RBF neural network to analyze the influencing factors of import and export in Guizhou Province. Taking the economic data of Guizhou Province from 2001 to 2020 as samples, RBF neural network is used as a tool to analyze the relationship between various influencing factors and total import and export volume. The results show that when the number of hidden layer neurons is 14 or more, the RBF neural network model accuracy is the highest, R2 is 0.7005, and MAPE is 0.0834. Among the first-level indicators affecting import and export, the importance of the regional internal economic level is the highest. Among the second-level indicators, the importance of scientific and technological progress is the highest, followed by GDP." @default.
- W4328096138 created "2023-03-22" @default.
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- W4328096138 date "2023-02-01" @default.
- W4328096138 modified "2023-09-30" @default.
- W4328096138 title "Analysis of Factors Affecting Import and Export of Underdeveloped Regions Based on RBF Neural Network - A Case Study of Guizhou Province" @default.
- W4328096138 doi "https://doi.org/10.54691/bcpbm.v37i.3555" @default.
- W4328096138 hasPublicationYear "2023" @default.
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