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直流电弧炉水冷炉壁温度曲线的神经网络辨识方法

A neural network identification method of the water-cooling furnace walls curves of Direct Current Electrical Arc Furnace
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摘要 阐述将改进的BP神经网络用于直流电弧炉水冷炉壁问题曲线的辨识。首先分析了偏弧偏向角和偏移量和水冷炉壁的温度的关系,然后通过辨识水冷炉壁的温度,根据公式确定偏弧的着弧点,计算出偏弧的偏向角和偏移量。该方法的应用为直流电弧炉偏弧控制打下坚实的基础。 BP neural network is improved and applied to the identification of the water-cooling furnace walls curves of the Direct Current Electrical Arc Furnace (DC EAF). First the relation of deflection angle and the offset of arc deflection with the temperature of the water-cooling furnace walls is analyzed, and then the arc point of arc deflection is validated by discriminating the temperature of the water-cooling furnace walls, and finally the deflection angle and the offset of the arc deflection are calculated out. It lays the foundation for the arc deflection control of DC EAF.
作者 郭飞 李华德
出处 《机电工程技术》 2005年第12期33-34,77,共3页 Mechanical & Electrical Engineering Technology
关键词 BP神经网络 直流电弧炉 系统辨识 偏弧 水冷炉壁 BP neural network Direct Current Electrical Arc Furnace (DC EAF) system identification arc deflection water-cooling furnace walls curves,
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参考文献3

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  • 3张立民.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1992..
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