| 基于优化BP神经网络的中子法检测煤中碳含量 |
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| 引用本文： | 桑海峰,王福利,刘林茂. 基于优化BP神经网络的中子法检测煤中碳含量[J]. 计量学报, 2006, 27(1): 73-76 |
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| 作者姓名： | 桑海峰 王福利 刘林茂 |
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| 作者单位： | 东北大学信息科学与工程学院教育部辽宁省流程工业综合自动化重点实验室,辽宁,沈阳,110004;东北师范大学物理系,吉林,长春,130024 |
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| 基金项目： | 国家重点基础研究发展计划(973计划) |
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| 摘 要： | 介绍了中于法快速检测煤中碳含量的方法。基于遗传算法（CA）优化的BP神经网络建立了煤中碳含量的检测模型，并结合电厂锅炉燃烧用煤的实测数据进行模型的验证研究。结果表明，该方法对煤中碳含量的检测精度达到了0．5％。
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| 关 键 词： | 计量学 碳含量 遗传算法 BP神经网络 中子法 特征γ射线 |
| 文章编号： | 1000-1158（2006）01-0073-04 |
| 收稿时间： | 2004-04-06 |
| 修稿时间： | 2005-02-02 |
Detection of Carbon Content in Coal with Neutron Method Based on an Optimized BP Neural Network |
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| SANG Hai-feng,WANG Fu-li,LIU Lin-mao. Detection of Carbon Content in Coal with Neutron Method Based on an Optimized BP Neural Network[J]. Acta Metrologica Sinica, 2006, 27(1): 73-76 |
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| Authors: | SANG Hai-feng WANG Fu-li LIU Lin-mao |
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| Affiliation: | 1 .College of Information Science and Engineering, Northeast University, Shenyang, Liaoning 110004, China; 2. Department of Physics. Northeast Normal University, Changchun, Jilin 130024, China |
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| Abstract: | The detection method of carbon content in coal with neutron method is introduced. The detection model of carbon content in coal based on BP neural network, which is optimized by genetic algorithms, is put forward. A study for verifying the model is made by comparison with the practical measured data of coal in power plant. The results show that the detection precision is 0.5 % with this model. |
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