Dynamic Model of Liquid Copper Temperature Based on Weighted LS-SVM

Aimed to the difficult temperature measurement of scrap copper smelting process, this paper proposed a method of dynamic prediction method of furnace temperature based on weighted least squares support vector machine (WLS-SVM). In this method, the main input and output variables of the process squared error is given different weights to overcome the impact of the training sample anomalies, and use PSO for WLS-SVM parameters optimization, enhanced ability to adapt of dynamic model for the nonlinear time-varying characteristics, improved the prediction accuracy of the model. Finally, simulated through actual operating data of scrap copper smelting process, and verified the effectiveness of the method.