Breakout prediction based on particle swarm optimization back propagation neural network in continuous casting process

Abstract

Aiming at the two terrible drawbacks of slow convergence and local optimal solution in the training process of BP neural network, particle swarm optimization algorithm was introduced to the training process of the BP neural network to improve its converge property, so a PSO-BP neural network was established, and then it was introduced into the breakout prediction system. The PSO-BP breakout prediction neural network model was trained and tested with the historical data collected from a steel plant. The results show that the convergence rate of the PSO-BP neural network model is significantly improved comparing the traditional BP neural network, and the feasibility of the model is verified by the testing result with the accuracy rate of 96.39% and the prediction rate of 100%.