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案例推理及迭代学习在层流冷却控制中的应用

DOI: 10.3724/SP.J.1004.2012.02032, PP. 2032-2037

Keywords: 层流冷却,卷取温度,案例推理,迭代学习

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Abstract:

?现有的卷取温度预报补偿模型和带钢批次间补偿模型中,由于案例推理(Case-basedreasoning,CBR)系统中检索特征权重系数采用人工凑试的方法,难以获得满意的补偿作用,且由于缺乏迭代学习的初始工况条件的匹配算法,难以进行准确匹配和有效迭代.因此,本文针对这两个问题,提出了基于神经网络技术的案例推理系统检索特征权重系数自动学习算法及迭代学习技术初始工况匹配算法,改进了卷取温度预报补偿模型和带钢批次间补偿模型,并采用国内某大型钢厂的现场实际数据进行实验研究.实验结果表明,与原有方法相比,带钢卷取温度的控制偏差减小了1.63℃,卷取温度精度控制在±10℃以内的命中率提高了14.5%.

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