An adversarial network used for drift correction in electronic nose

Abstract

The electronic nose (E-nose) is an important means for machines to sense odor signals, but it faces a serious problem that limits its development: sensor drift. Drift refers to the long-time and irregular changes in the response signal of the gas sensor to the same analyte, and it will bring serious performance degradation to the E-nose. To reduce the effect of drift, we proposed an entropy minimization model based on adversarial networks (EMAD) which reduced the distribution difference between data with and without drift through adversarial training. EMAD consists of a generator and a classifier. To simplify the structure and improve the prediction diversity, the classifier is reused as a discriminator for adversarial training while learning the domain invariant features from the source domain. EMAD reduced the distribution difference between original data and drift data while preserving the features for classification. Experiment results on the public dataset showed that EMAD effectively eliminated the performance degradation caused by drift and achieved the highest classification accuracy compared with other advanced methods.