| 基于灰色神经网络的烧结矿碱度组合预测 |
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| 引用本文： | 鲍雅萍,马金元,宋 强.基于灰色神经网络的烧结矿碱度组合预测[J].控制理论与应用,2008,25(4):791-793. |
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| 作者姓名： | 鲍雅萍 马金元 宋 强 |
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| 作者单位： | 1. 安阳工学院机械工程系,河南,安阳,455000
2. 安阳工学院电子信息与电气工程系,河南,安阳,455000 |
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| 摘 要： | 针对钢铁生产过程中烧结矿碱度检测的难题,利用灰色预测的GM(I,1)模型与BP神经网络进行组合,建立了灰色神经网络的烧结矿碱度组合预测模型,选取10W与矿碱度有关的输入变量,对这些变量分别进行灰色GM(1,1)预估,再进行BP神经网络预测,获得烧结矿碱度预测结果,仿真结果的相对误差小于0.005%.
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| 关 键 词： | 灰色模型 神经网络 组合预测模型 烧结矿 碱度 |
| 收稿时间： | 2007/4/27 0:00:00 |
| 修稿时间： | 2007/8/25 0:00:00 |
Combination forecasting of sintered ore alkalinity based on grey neural network |
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| BAO Ya-ping,MA Jin-yuan and SONG Qiang.Combination forecasting of sintered ore alkalinity based on grey neural network[J].Control Theory & Applications,2008,25(4):791-793. |
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| Authors: | BAO Ya-ping MA Jin-yuan and SONG Qiang |
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| Affiliation: | Department of Mechanical Engineering, Anyang Institute of Technology, Anyang Henan 455000, China;Department of Electrical Engineering, Anyang Institute of Technology, Anyang Henan 455000, China;Department of Mechanical Engineering, Anyang Institute of Technology, Anyang Henan 455000, China |
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| Abstract: | To predict the alkalinity of sintered ore accurately in sintered process,a combination grey neural network forecasting model of grey neural network is proposed by combining the grey model GM(1,1)with BP(Back Propagation) neural network.Ten factors relating with the sintered ore alkalinity are selected as the input variables.These variables are estimated on grey model GM(1,1)respectively and the alkalinity of sintered ore is forecasted on BP neural network based on all of these estimated data.The results of simulation show that the relative error is less than 0.005%. |
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| Keywords: | grey model neural network combination forecasting model sintered ore alkalinity |
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