Application of PSO Algorithm in Optimizing BP Neural Network

BP neural network is a widely used means in the field of data prediction, and it is very important to improve the fitting ability and prediction ability of BP neural network to nonlinear problems in practical application. Aiming at the problems of slow convergence, low accuracy and poor fitting effect of traditional BP neural network, this paper proposes the PSO-BP model of BP neural network with PSO algorithm. This model uses the PSO algorithm to find the optimal parameter group for the BP neural network for the best model performance. Comparing this model with the BP model, the results show that the PSO-BP model is about 5% and 40% better in goodness of fit and prediction accuracy, respectively, which provides a useful scheme for data prediction research.