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
针对起重机减速齿轮箱的磨损过程具有非线性与时变性,传统磨损趋势预测方法无法有效兼顾预测精度与执行效率的问题,提出了一种基于组合核函数的在线支持向量机回归(online support vector regression,OSVR)预测算法。OSVR的在线学习算...针对起重机减速齿轮箱的磨损过程具有非线性与时变性,传统磨损趋势预测方法无法有效兼顾预测精度与执行效率的问题,提出了一种基于组合核函数的在线支持向量机回归(online support vector regression,OSVR)预测算法。OSVR的在线学习算法能够适应时间序列的时变性并提高执行效率,同时可利用不同的核函数性能,通过组合模型提高预测精度。采用实际齿轮箱铁谱数据对预测算法进行验证,结果表明,基于组合核函数的OSVR预测算法具有很好的预测精度和适应性,能有效预测起重机齿轮箱的磨损故障,且相比于单一OSVR算法和灰色神经网络组合算法有更高的效率和预测精度。展开更多
文摘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.
文摘针对起重机减速齿轮箱的磨损过程具有非线性与时变性,传统磨损趋势预测方法无法有效兼顾预测精度与执行效率的问题,提出了一种基于组合核函数的在线支持向量机回归(online support vector regression,OSVR)预测算法。OSVR的在线学习算法能够适应时间序列的时变性并提高执行效率,同时可利用不同的核函数性能,通过组合模型提高预测精度。采用实际齿轮箱铁谱数据对预测算法进行验证,结果表明,基于组合核函数的OSVR预测算法具有很好的预测精度和适应性,能有效预测起重机齿轮箱的磨损故障,且相比于单一OSVR算法和灰色神经网络组合算法有更高的效率和预测精度。
