Application of neural network trained by chaos particle swarm optimization to prediction of silicon content in hot metal

A new approach is proposed to predict the silicon content in hot metal with neural network trained by chaos particle swarm optimization. Firstly, an advanced particle swarm optimization algorithm based on chaos search(CPSO) is presented to enhance the local searching ability and improve the convergence speed. Then CPSO is applied to train neural network and a model to predict the silicon content in hot metal is constructed, the data of the model are collected from No.3 BF in Panzhihua Iron and Steel Group Co.. The results show that the neural network prediction model has good results and the prediction precision can meet the requirement of practical production.