在對(duì)氣體進(jìn)行長(zhǎng)期在線監(jiān)測(cè)的場(chǎng)合,所要解決的關(guān)鍵問題之一就是氣體傳感器特性漂移的
抑制。傳感器特性漂移會(huì)給氣體的測(cè)量和識(shí)別帶來誤差。對(duì)此,本文提出一種動(dòng)態(tài)在線標(biāo)定法。該方法能夠?qū)鞲衅髌乒收献龀雠卸?同時(shí)可實(shí)現(xiàn)測(cè)量誤差的修正。文中對(duì)該方法進(jìn)行了詳細(xì)的描述和論證。計(jì)算機(jī)仿真結(jié)果表明了該方法的有效性。
關(guān)鍵詞: 氣體傳感器; 神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)器; 混合氣體識(shí)別
Abstract : The drift should be addressed as one of the most serious impairments for gas sensor. When drift exists , the gas measurement or identification result may cause error. To solve this , a dy2 namic on2line calibration method is proposed in this paper. By this method the sensor drift can be detected accurately and the measurement error caused by sensor drift may be corrected at the same time. Detailed descriptions of this method are given. The simulation results show that the method is valid.
Key words : Gas sensor ; Neural network predictor ; Mixed gas identification