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基于布谷鸟算法与bp神经网络的煤灰变形温度预测

, PP. 1423-1430

Keywords: 煤灰,灰变形温度,bp神经网络,布谷鸟算法,添加剂,配煤

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Abstract:

?以120种煤样为数据基础,采用布谷鸟算法(cs)优化bp(backpropagation)神经网络,建立了csbp模型对单煤、煤掺添加剂和配煤等3类样本的煤灰变形温度(dt)样本进行预测。模型以煤灰化学成分及其组合参数等13个变量作为输入量,以变形温度(dt)作为输出量。csbp模型预测结果与bp神经网络模型预测结果进行对比发现,无论是单煤、煤掺添加剂还是配煤,csbp模型较bp模型对煤灰变形温度(dt)的预测都更加精准,平均相对误差分别达到了3.11%、4.08%和4.22%。另外,对比3类样本预测结果发现,无论是csbp模型还是bp模型,相比单煤预测而言,煤掺添加剂及配煤的预测误差都有明显的增加。

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