Short-term prediction of BP neural network optimized by improved particle swarm optimization algorithm

Abstract

Salps algorithm was used to optimize the initial weights and thresholds of BP neural network, to speed up the BP neural network parameters of PID controller, and finally obtain the optimal parameters. The variable weight is integrated into the iterative process to expand the early search range and improve the late search accuracy. In Matlab2019 simulation environment, BP neural network and BP neural network optimized by Salps were compared in the prediction effect. The results show that the optimized BP neural network has higher prediction accuracy than the traditional BP neural network.