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机构地区:[1]西安建筑科技大学冶金工程学院,陕西西安710055
出 处:《冶金丛刊》2011年第6期16-19,共4页Metallurgical Collections
基 金:陕西省工业攻关项目支助(2009K07-20)
摘 要:通过分析层流冷却过程中钢板终冷温度的各种影响因素,建立了终冷温度的BP神经网络预报模型,确定了网络的输入、输出量。运用"试凑法"对不同网络结构进行了训练,最终确立了神经网络模型结构,并用所建模型对终冷温度进行预测。结果显示:网络预测值与现场实测值比较逼近,误差基本维持在±7℃,证明所建模型是合适的,可以用于生产现场的指导。By analyzing the various factors of final cooling temperature of steel in the laminar cooling, the BP neural network prediction model of final cooling temperature was established and the input and output of the network were determined. The neural network model structure was established after the different network structures were trained by "trial and error", and the final cooling temperature was predicted by the model. The re- suits showed that the network predictive value and the measured value were approximate, and the error was maintained at +7℃. The model was appropriate and could be used to guide the production.
分 类 号:TP393.09[自动化与计算机技术—计算机应用技术] TG334.96[自动化与计算机技术—计算机科学与技术]
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