The regression accuracy of the support vector regression (SVR) models depends on a proper setting of its parameters to a great extent. An optimal selection approach of v-SVR parameters was put forward based on chaos particle swarm optimization (CPSO) algorithm. Then a model based on v-SVR to predict the silicon content in hot metal was established, and the optimal parameters of the model was searched by CPSO. The data of the model were collected from No.3 BF in Panzhihua Iron and Steel Group Co.. The results showed that the proposed prediction model has better prediction results than neural network trained by particle swarm optimization and v-SVR, the percentage of samples whose absolute prediction error is less than 0.03 when predicting silicon content by v-SVR model is higher than 90%, it indicated that the prediction precision can meet the requirement of practical production.
