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On-line forecasting model for zinc output based on self-tuning support vector regression and its application
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作者 胡志坤 桂卫华 彭小奇 《Journal of Central South University of Technology》 2004年第4期461-464,共4页
An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In ... An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace. 展开更多
关键词 imperial smelting furnace support vectors regression sequential minimal optimization zinc output on-line forecasting
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