The support vector regression based on the chaos particle swarm optimization algorithm for the prediction of silicon content in hot metal
铁水硅含量的混沌粒子群支持向量机预报方法

An optimal selection approach of support vector regression parameters is proposed based on the chaos particle swarm optimization(CPSO) algorithm; A model based on the support vector regression to predict the silicon content in hot metal is established; and the optimal parameters of which is searched by CPSO. The data of the model are also collected from the No.3 BF in Panzhihua Iron and Steel Group Co. The results show that the proposed prediction model has better prediction results than the neural network trained by particle swarm optimization and least squares support vector regression algorithm; the percentage of samples with absolute prediction error less than 0.03 is higher than 90%, when predicting the silicon content by the proposed model. This indicates that the prediction precision can meet the requirement of practical production and demonstrates that the CPSO is an effective approach for parameter optimization of support-vector regression.