The Application of Neural Network Algorithm in Computer Mathematical Modeling

Abstract

The application research of neural network algorithm in computer mathematical modeling has made extensive development. With its strong learning and approximation ability, it has shown great potential and application prospect in many fields. Through literature review and comparative analysis, this study compared the performance of CNN and RNN in the mathematical model of financial risk. The two algorithms have different performances under the same mathematical model. The results of the study showed that the accuracy of risk assessment of CNNS was between 93% and 98%, while the accuracy of RNN was between 89% and 96%, and the performance of CNNS was between 3-6s and RNN was between 4-8s on the assessment time. For the same neural network algorithm model, the two algorithms show different performance in financial risk assessment, because the weight parameter sharing in CNN can significantly reduce the number of parameters in the model, thus reducing the risk of overfitting.