| 基于气体传感器阵列的矿井可燃混合气体分析 |
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| 引用本文： | 叶小婷,张青春,童敏明.基于气体传感器阵列的矿井可燃混合气体分析[J].煤矿安全,2012,43(2):16-19. |
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| 作者姓名： | 叶小婷 张青春 童敏明 |
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| 作者单位： | 1. 淮阴工学院电子与电气工程学院,江苏淮安,223003
2. 中国矿业大学信电学院,江苏徐州,221116 |
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| 基金项目： | 国家自然科学基金重大国际合作研究资助项目 |
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| 摘 要： | 由于气体传感器的选择性差,交叉敏感严重,单一BP神经网络识别方法存在识别能力低,分析误差较大,在非期望节点有噪声输出等难题,影响气体分析的精度和效果。对基于常规BP神经网络的定量分析方法进行了改进,提出一种双层复合神经网络的气体分析模型,并以矿井中常见的H2S,CO和CH4 3种可燃混合气体为实验对象,进行混合气体的定量分析。实验结果表明,基于双层复合神经网络的可燃混合气体定量分析最大相对误差为4.4%,大大提高了定量分析精度。
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| 关 键 词： | 气体传感器阵列 神经网络 可燃混合气体 模式识别 |
Analysis of Mixed Inflammable Gases in Mine Based on Gas Sensor Arrays |
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| YE Xiao-ting,ZHANG Qing-chun,TONG Min-ming.Analysis of Mixed Inflammable Gases in Mine Based on Gas Sensor Arrays[J].Safety in Coal Mines,2012,43(2):16-19. |
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| Authors: | YE Xiao-ting ZHANG Qing-chun TONG Min-ming |
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| Affiliation: | 1.School of Electronic and Electrical Engineering,Huaiyin Institute of Technology,Huaian 223003,China;2.School of Information and Electrical Engineering,China University of Mining &Technology,Xuzhou 221116,China) |
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| Abstract: | Poor selectivity and cross sensitivity are problems of gas sensors,general BP(back propagation) neural network has low recognition ability and its error is too high to accept.A hybrid two layers neural networks is developed to improve the quantitative analysis of general BP neural network.Using two layers neural networks,three mixed inflammable gases in mine are adopted for the quantitative analysis.The result shows that recognition ability of the neural network is increased and the maximum relative error of quantitative analysis is 4.4%,the precision of quantitative analysis is increased. |
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| Keywords: | gas sensor arrays neural network mixed inflammable gase pattern recognition |
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