Diagnosis System for Alumina Reduction Based on BP Neural Network
Diagnosis System for Alumina Reduction Based on BP Neural Network
复制标题
基于BP神经网络的氧化铝还原诊断系统
DOI：
10.4304/jcp.7.4.929-933
复制
发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
崔琳
中科院分区：
文献类型：
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作者：
曾水平;崔琳
基于遗传算法优化BP神经网络，开发了氧化铝还原过程的故障诊断系统。该神经网络采用由电池电阻计算的频率能量组成的特征向量作为10个输入，3个电池状态作为3个输出。通过工业采样数据对神经网络进行了验证。结果表明，该系统的准确率大于80%，能够满足铝型材生产的要求。设计了故障诊断软件，并在某铝冶炼厂进行了应用.
The diagnosis system for the alumina reduction was developed on the basis of BP neural network with optimization by genetic algorithm. The neural network used the characteristic vectors composed of the frequency energy calculated from cell resistance as 10 inputs and three cell statuses as 3 outputs. The neural network was certified by industrially sampling data. The results showed the accuracy ratio was larger than 80%, which can meet the requirements in the aluminum production. The diagnosis software was designed and applied in an aluminum smelter.
DOI：
10.1109/wcica.2008.4594446
发表时间：
2008-06
期刊：
2008 7th World Congress on Intelligent Control and Automation
影响因子：
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作者：
Zeng Shuiping;Liu Jinhong
通讯作者：
Zeng Shuiping;Liu Jinhong