基于自校正支持向量回归的锌产量在线预报模型及应用

提出了基于自校正支持向量回归的密闭鼓风炉锌产量在线预报模型，以便根据预报结果来调整参数，实现锌产量最大．在该模型中，支持向量回归的数学模型被转换成与支持向量分类一样的格式，然后采用简化的SMO方法训练回归系数向量a-a*和阈值b，并在训练过程中动态调整惩罚系数C．最后，给出锌产量的在线预报算法．仿真结果表明，该预报模型在只有较少的样本数的情况下，在有效误差范围内预报精度能达到90％，且具有很好的实时性．

An Online Forecasting Model for Zinc Output Based on Self-tuning Support Vector Regression and Its Application

An online forecasting model based on self-tuning support vectors regression (SVR) for zinc output in imperial smelting furnace is put forward with the aim of maximizing zinc output by adjusting operational parameters. In this model, the mathematical model of SVR is converted into the same format as that of support vector machines for classification. A simplified sequential minimal optimization (SMO) for classification is applied to train the regression coefficient vector a-a* and threshold b. Penalty parameter C can be tuned dynamically with the forecasting result until the training process ends. The online forecasting algorithm for zinc output is also presented. In spite of a relatively small industrial data set, the simulation result shows that the effective error is less than 10% with a remarkable performance of real time.