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城市危险品气体辨识技术的研究
  • ISSN号：1002-1841
  • 期刊名称：《仪表技术与传感器》
  • 时间：0
  • 分类：TP2[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
  • 作者机构：[1]大连民族大学机电工程学院,辽宁大连116600, [2]大连理工大学电子科学与技术学院,辽宁大连116024, [3]大连民族大学信息与通信工程学院,辽宁大连116600
  • 相关基金：国家自然科学基金项目（61176068,61508081）;辽宁省自然科学基金项目（2015020096）;大连民族大学自主科研基金项目（DC201502010302）
作者： 杜海英[1,2], 孙炎辉[2,3], 张汝波, 刘冠群
关键词： 支持向量机, 气体传感器, 气体辨识, 定性与定量分析, support vector machines, gas sensor, gas identification, qualitative and quantitative analysis
中文摘要：

城市危险品气体检测对于城市安全至关重要。利用SnO2,In2O3,SnO2/NiO,SnO2/In2O3,SnO2/Pa,SnO2/Sb 6种旁热式气敏元件组成的气体传感器阵列,分别采用BP神经网络、支持向量机（SVM）和极限学习机（ELM）3种气体辨识方法实现对城市危险气体中常见的3种气体（NH3,HCOH,C7H8）进行定性的识别。测试结果表明：基于SVM与ELM的气体辨识技术对于含有低浓度甲醛的混合气体定性识别率达100%,且在收敛速度、泛化性能等方面较BP神经网络有明显提高。

英文摘要：

Detection of urban hazardous gases are very important for urban safety.In this paper,the gas sensors array was composed of six types of side-heating gas-sensitive sensors which are made of SnO2,In2O3,SnO2/NiO,SnO2/In2O3,SnO2/Pa,SnO2/Sb.The three kinds of gas recognition mothods,Back-Propagation NN（BP）,extreme learning machine（SVM） and extreme learning machine（ELM） were uesd to indentify three kinds of urban hazardous gases（NH3,HCOH,C7H8）.The detection result shows that gas recognition mothods based on SVM and ELM are improved than BP in generalization performance and convergence speed,the qualitative recognition rate to low concentration of formaldehyde in the mixed gas is 100%.

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期刊信息
  • 《仪表技术与传感器》
  • 中国科技核心期刊
  • 主管单位:沈阳仪表科学研究院
  • 主办单位:沈阳仪表科学研究院
  • 主编：刘凯
  • 地址：沈阳市大东区北海街242号
  • 邮编：110043
  • 邮箱：bjb@17sensor.com
  • 电话：024-88718630 88718620
  • 国际标准刊号：ISSN：1002-1841
  • 国内统一刊号：ISSN：21-1154/TH
  • 邮发代号:8-69
  • 获奖情况:
  • 2007年获得北方优秀期刊奖,2007年荣获机械工业期刊质量评审一等奖
  • 国内外数据库收录:
  • 波兰哥白尼索引,美国剑桥科学文摘,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:16968