A Novel Memory Mechanism for Postponing the Drift of Chemical Gas Sensors

Abstract

Sensor drift is one of the important problems affecting the stability of gas sensing and detection system. We proposed a novel memory mechanism, which can be combined with the traditional machine learning classifier to deal with and improve the sensor drift problem. Based on the memory mechanism, we established recognition model, which adopts support vector machine as base-classifier, to achieve better classification effect and eliminate the imbalance of data set. The Gas Sensor Array Drift Dataset from UCI Machine Learning Repository was selected for experimental verification. Through experiments, the method proposed in this paper can improve the classification effect in the short and medium term, postpone the negative effects brought by drift to a certain extent, and then extend the service life of the gas sensor.