Application of QGA-BP Neural Network in Debt Risk Assessment of Government Platforms

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Author(s): Qingping Li (Huainan Normal University, China), Ming Liu (Huainan Normal University, China)and Yao Zhang (Southwestern University of Finance and Economics, China)
Copyright: 2024
Volume: 19
Issue: 1
Pages: 18
Source title: International Journal of Information Technology and Web Engineering (IJITWE)
Editor(s)-in-Chief: Ghazi I. Alkhatib (The Hashemite University, Jordan (retired))
DOI: 10.4018/IJITWE.335124

Keywords: Computer Science & IT / Information Science Reference / Web Technologies / Web Technologies & Engineering

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Abstract

How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.

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Application of QGA-BP Neural Network in Debt Risk Assessment of Government Platforms

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