摘要
文中提出一种基于独立成分分析的非监督气体模式识别方法,用多元统计理论中的独立成分分析(independ-ent component analysis-ICA)来分析金属氧化物半导体气体传感器阵列响应数据,进而实现对不同种类气体的分类。对所设计的电子鼻实验系统测量得到的气体传感器阵列稳态响应数据进行了白化和快速定点独立成分分析(FastICA)处理。实验结果表明该方法对区分一氧化碳(CO)、甲烷(CH_4)和氢气(H_2)3种气体有很高的识别率。
An ICA-based unsupervised pattern recognition method was proposed. Independent component analysis （ICA） as a multivariate statistical tool was used to analyze the steady-state responses generated by an array of commercial metal-oxide gas sensors in an electronic nose system and to classify the different gases. Whitening and FastICA iteration are used to process the gas sensor array measurement data obtained by an experimental electronic nose system. The technique is demonstrated by discriminating three gases carbon monoxide （CO）, methane （CH4） and hydrogen （H2） with a high success rate.
出处
《仪表技术与传感器》
CSCD
北大核心
2009年第B11期41-43,共3页
Instrument Technique and Sensor
基金
国家自然科学基金资助项目(60871034)
关键词
传感器信号处理
气体模式识别
独立成分分析
sensor signal processing
gas pattern recognition
independent component analysis
