| Title |
An FWA-BP Network Providing an Evolutionary Game System for Enterprise Information Management |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.3.285 |
| Keywords |
Enterprise information management; Evolutionary game; Fireworks algorithm; Levenberg-Marquardt algorithm; Neural network; Principal component analysis method; Performance simulation |
| Abstract |
With the rapid development of society, the competition among various enterprises is also constantly strengthening. In view of this, research on applying various artificial intelligence methods to enterprise information management game systems came into being. In this study, principal component analysis reduces the dimensionality of each data method. Then, the fireworks algorithm improves and adjusts the numbers involved in back propagation. The Levenberg-Marquardt algorithm promotes training accuracy in the model, which jumps out of the local minimum and obtains the optimal solution. Research results show that when the number of iterations is set to 300, the model reaches the target error after only 110. The Fireworks Algorithm?Back Propagation (FWA-BP) test results indicate that while some samples had a prediction deviation of over 10, the variance between other models' predictions and the expected value was not significant, demonstrating a fitness degree of 0.9254. Comparing the index evaluation results of different algorithms, the root mean square error (RMSE) predicted by the FWA-BP pattern was about 7.775, and the index effect of MSE was significantly better than the other models. The above shows that the research model has high feasibility and superiority, the advantage of high-speed operation, and provides certain technical means for enterprises to promote great circulation of the international economy. |