Construction of Economic Data Management System Based on BP Neural Network

Area of Science:

• Computational economics
• Artificial intelligence in finance
• Neural network applications

Background:

• Economic data management systems require advanced technological solutions.
• Understanding human nervous system principles can inform computational models.
• Existing systems may lack the adaptability for complex economic data.

Purpose of the Study:

• To develop and implement a BP neural network for economic data management.
• To optimize algorithms for weight rule 2BP grid construction.
• To explore the application of neural networks in enhancing enterprise economic operations.

Main Methods:

• Construction of a BP neural network tailored for economic data management.
• Utilizing an open platform algorithm for optimizing weight rule 2BP grid construction.
• Simulating human brain nervous system characteristics within the neural network architecture.

Main Results:

• The developed BP neural network is suitable for economic data management systems.
• Neural network application improves enterprise management levels, benefits, and profits.
• Demonstrated utility in economic early warning, risk analysis, and credit evaluation.

Conclusions:

• BP neural networks offer a robust solution for modern economic data management.
• The approach enhances decision-making capabilities in areas like risk assessment and competitiveness.
• Neural network integration is key to improving overall enterprise economic performance.