| 基于粗糙集-BP神经网络的浮选槽液位预测 |
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| 引用本文： | 刘潭,张勇.基于粗糙集-BP神经网络的浮选槽液位预测[J].鞍山科技大学学报,2010(5). |
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| 作者姓名： | 刘潭 张勇 |
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| 作者单位： | 辽宁科技大学电子与信息工程学院; |
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| 基金项目： | 辽宁省高校创新团队支持计划项目(2008T091) |
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| 摘 要： | 针对反浮选过程中浮选槽液位指标难以建立精确的数学模型、常规检测方法不能有效控制问题,提出一种将粗糙集与BP神经网络理论相结合方法1],建立反浮选液位软测量模型。从浮选过程积累的数据中获取过程知识,通过粗糙集属性约简对训练样本数据进行处理,根据结果确定BP网络的输入、输出、隐层神经元数,从得到的优化设定自动更新浮选槽液位控制回路的设定值,避免了人工控制的不稳定性和不精确性。此方法应用于某浮选厂,满足了液位预测要求的精度,在液位控制、经济指标提高及浮选过程稳定等方面取得了明显的效果。
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| 关 键 词： | 浮选槽液位 粗糙集 BP神经网络 模型预测 |
Prediction of flotation cell liquid level with rough set and BP neural network |
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| LIU Tan,ZHANG Yong.Prediction of flotation cell liquid level with rough set and BP neural network[J].Journal of Anshan University of Science and Technology,2010(5). |
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| Authors: | LIU Tan ZHANG Yong |
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| Institution: | LIU Tan,ZHANG Yong(School of Electronic and Information Engineering,University of Science and Technology Liaoning,Anshan 114051,China) |
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| Abstract: | Anti-flotation process for the flotation level indicator is difficult to establish an accurate mathematical model,and the conventional method of detection and control is difficult to control.A new recovery prediction method based on rough set theory with the combination of BP neural network is proposed in this paper.The experience of flotation process can be grasped more profoundly from lots of historical process data,the sample data is processed by rough set attribute reduction,which determines the input,o... |
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| Keywords: | flotation cell liquid level rough sets BP neural network model prediction |
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