BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model

Financial early warning mechanism is of great significance to the long-term healthy development and stable operation of listed enterprises. This paper adopts the logistic regression early warning model and BP neural network early warning model. Based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspectives of model accuracy and variable importance. Through the comparative analysis of the empirical results of the three methods, it can be seen that the simulated annealing algorithm has many advantages. The combination of the simulated annealing algorithm with multithreading, data compression, and segmentation greatly improves the efficiency of the algorithm and shortens the running time. Using the logistic regression early warning model and BP neural network early warning model and based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspective of model accuracy and variable importance. The results show that the three index dimensions of the BP neural network optimized by the simulated annealing algorithm have good discrimination ability to financial status. The BP neural network early warning model optimized based on the simulated annealing algorithm has good prediction accuracy and good practical significance.

Automated summary

Financial early warning mechanism is crucial for the long-term healthy development and stable operation of listed enterprises. This paper compares the prediction effects of Logistic regression model and BP neural network model, and concludes that the simulated annealing algorithm has many advantages in optimizing the BP neural network model. The combination of simulated annealing algorithm with multithreading, data compression, and segmentation significantly improves the algorithm efficiency and reduces running time.