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- W4313156280 abstract "The research focuses on using BP Neural Network Technology and Principal Component Analysis to prevent debt crisis. Using AI (artificial intelligence technology) to establish an implicit debt prediction model is a new topic. This paper establishes an effective indicator system, uses principal component analysis (PCA) to extract comprehensive indicators, then uses the BP neural network model to derive the predicted value of the local government implicit debt in the past five years, and finally compares the predicted value with the estimated value. The results show that in 2017, 2019, and 2020, the estimated implicit debts of local governments did not reach the warning level, and the risks were controllable. In 2016 and 2018, it exceeded the warning forecast value, which is worthy of attention. This paper serves as an example to be extended and applied to the construction of early warning system in various places." @default.
- W4313156280 created "2023-01-06" @default.
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- W4313156280 date "2022-05-01" @default.
- W4313156280 modified "2023-09-23" @default.
- W4313156280 title "Application of BP Neural Network Technology and Principal Component Analysis on Local Government Implicit Debt Risk Prediction" @default.
- W4313156280 cites W2113929764 @default.
- W4313156280 doi "https://doi.org/10.1109/jcice56791.2022.00022" @default.
- W4313156280 hasPublicationYear "2022" @default.
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