刘建国,安振涛,张倩.基于传感器阵列的可燃混合气体RBF网络分析[J].装备环境工程,2013,10(3):!13-116. LIU Jian-guo,AN Zhen-tao,ZHANG Qian.Analysis of Mixed Inflammable Gases Based on Sensor Array and RBF Network[J].Equipment Environmental Engineering,2013,10(3):!13-116.
基于传感器阵列的可燃混合气体RBF网络分析
Analysis of Mixed Inflammable Gases Based on Sensor Array and RBF Network
投稿时间:2013-01-21  修订日期:2013-05-01
DOI:10.7643/issn.1672-9242.2013.03.027
中文关键词:  可燃气体  气体分析  传感器阵列  RBF神经网络
英文关键词:inflammable gas  gas analysis  sensor array  RBF neural network
基金项目:
作者单位
刘建国 军械工程学院,石家庄050003 
安振涛 军械工程学院,石家庄050003 
张倩 军械工程学院,石家庄050003 
AuthorInstitution
LIU Jian-guo Ordnance Engineering College,Shijiazhuang050003,China 
AN Zhen-tao Ordnance Engineering College,Shijiazhuang050003,China 
ZHANG Qian Ordnance Engineering College,Shijiazhuang050003,China 
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中文摘要:
      为克服传感器阵列在混合气体检测中的交叉敏感现象,采用具有最佳逼近和全局最优性能的RBF神经网络对传感器阵列的输出信号进行分析。建立了多种可燃气体分析的数学模型,并对CO,H2和CH4的混合气体样本进行了实验。结果表明,传感器阵列和RBF神经网络处理单元构成的气体分析系统可以较好地实现对可燃混合气体的分析,误差不大于2%。
英文摘要:
      In order to overcome the cross-sensitivity phenomenon of sensor array in mixed gas detection, RBF neural network having the best approximation and performance of global optimal was used to analyze of the signal. A mathematical model for a variety of combustible gas analysis was established, and the gas mixture of CO, H2, and CH4 was analyzed. The results showed that the gas analysis system can better analyze combustible gas mixture; the error is less than2%.
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