| 基于多传感器数据融合技术的烧结矿碱度预报模型的研究 |
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| 引用本文： | 李振宇. 基于多传感器数据融合技术的烧结矿碱度预报模型的研究[J]. 安阳工学院学报, 2006, 0(1): 33-36 |
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| 作者姓名： | 李振宇 |
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| 作者单位： | 安阳工学院,河南,安阳,455000 |
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| 摘 要： | 烧结矿碱度的测量是钢铁工业中的关键和难点,况且又容易受到烧结几乎每一个操作环节的影响。利用BP神经网络进行多传感器数据融合的烧结矿碱度的预报模型,可对现场实际数据进行仿真,该方法准确性高,泛化能力广,具有很强的实用性和推广价值。
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| 关 键 词： | 多传感器数据融合 神经网络算法 碱度 权值和阈值 样本数据 |
| 文章编号： | 1673-2928（2006）01-0033-04 |
| 收稿时间： | 2005-09-11 |
| 修稿时间： | 2005-09-11 |
Study of Prediction Model of R in Sintering Process Based on Multisensor Data Fusion |
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| LI Zhen-yu. Study of Prediction Model of R in Sintering Process Based on Multisensor Data Fusion[J]. Journal of Anyang Institute of Technology, 2006, 0(1): 33-36 |
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| Authors: | LI Zhen-yu |
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| Abstract: | The measurement of R in sintering process is difficult to control , on the other hand, it is easily to be disturbed by almost process steps. A prediction model of R in sintering process based on BP neural network is proposed to judge the trend of R. The application result shows that the prediction with this method can achieve higher robust, better utility and expensive value. |
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| Keywords: | multisensor data fusion neural network algebra R synaptic weights and bias sample data. |
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