Application analysis of digital fund prediction model based on neural network
Yu Liu, Jing Xiao · Concurrency and Computation Practice and Experience · 2022
Summary With the development of the economy, digital funds have developed rapidly, and traditional big data processing technologies have been unable to effectively solve the update of digital funds information. This research uses neural network to study the digital fund forecasting model based on big data technology and deep learning algorithm. First, on the basis of the feedforward neural network model, the network structure and network parameters are optimized to effectively improve the algorithm's ability to learn the characteristics of digital capital information. Through the first‐order approximation, the number of iterations in the model parameter update is effectively reduced, and the computational efficiency of the algorithm is improved. By improving the model network structure, increasing the number of neurons in the hidden layer, and improving the dynamic change learning ability of the algorithm, the dynamic characteristics of the incremental file information can be effectively obtained. Through the experimental simulation calculation, it can be seen that compared with the traditional algorithm, the algorithm proposed in this study has certain advantages in terms of convergence speed, convergence ability and solution error, and the algorithm has good stability and strong inheritance, and is suitable for digital capital forecasting aspects of research.