东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

基于气体传感器阵列的混合气体定量分析

期刊名称：仪器仪表学报, 27(7),666-670, 2006.7
时间：0
分类：TP212[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]电子科技大学光电信息学院新型传感器教育部重点实验室,成都610054
相关基金：国家自然科学基金（60372002,60425101）资助项目.
相关项目：传感技术及其系统

关键词：气体传感器阵列, BP神经网络, 定量分析, gas sensor array Back-Propagation neural network quantitative analysis

中文摘要：

优选CO和H2气体敏感的半导体气体传感器组成阵列，建立实时数据采集系统，结合BP神经网络模式识别技术，实现了混合气体组分的定量分析。讨论了不同响应时间下的阵列输出值、不同的数据预处理算法及不同的神经网络结构等主要影响因素对网络输出结果的影响。结果表明，采用RRD预处理算法对3min响应时间下的阵列输出值进行处理，再输入到有12个隐层神经元数的3层BP神经网络进行训练，预测的效果最好。该处理模式能较准确地完成CO和H2混合气体组分的定量分析。

英文摘要：

The semiconductor gas sensors sensitive to carbon monoxide and hydrogen were chosen to compose the gas sensor array, and an on-line data acquisition system was constructed. Combining with the pattern recognition techniques of back-propagation （BP） neuron network, the system was used to carry out the quantitative analysis of the partial gas concentration in a mixture. The main effect factors to the outputs of BP neuron network, such as array outputs under different response time,the different pre-processing algorithms and the different structures of the neural network, are discussed in this paper. It is shown that the best prediction results are obtained when the array output under 3min response time is processed using RRD pre-processing algorithm and used as the actual input of the neural network, then the training and testing of this three-layer BP neuron network with 12 neurons in hidden layer are performed. This processing mode can accomplish the quantitative analysis of the partial gas concentration of the mixture （hydrogen and carbon monoxide） accurately.