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基于非线性PCA的微气体传感器阵列信号处理
Nonlinear PCA based micro gas sensor array signal processing
【摘要】 在线性叠加模型基础上提出了气体传感器对混合气体的非线性叠加模型,并引入了非线性主成分分析(NonlinearPrincipalComponentAnalysis,NLPCA)法对微传感器阵列的信号进行处理。使用该模型对由4个微热板式气体传感器组成的阵列的信号进行了分析,对照基于线性叠加模型的主成分分析法(PrincipalComponentAnalysis,PCA)的识别结果,说明该方法能够提高对混合气体识别和量化的准确度。
【Abstract】 A nonlinear superposition model was proposed based on the common linear additive model the micro gas sensor array signal processing to improve the precision of quantification and identification. According to the nonlinear model, the Nonlinear Principal Component Analysis (NLPCA) was proposed to process the response signals obtained from a 4 Micro-hotplate (MHP) based gas sensor array. Com-pared with the analyzing results obtained from Principal Component Analysis (PCA), which bases onthe linear additive model, the accuracy of gas component identification and concentration quantification are improved greatly.
【Key words】 gas sensor array; gas mixture analysis; nonlinear principal component analysis;
- 【文献出处】 功能材料与器件学报 ,Journal of Functional Materials and Devices , 编辑部邮箱 ,2005年01期
- 【分类号】TP212
- 【被引频次】7
- 【下载频次】313