Drift counteraction for an electronic nose

Most gas sensors do not give stable responses over a long period of time. This drift causes electronic noses to classify gases wrongly after some period of time. In this paper this problem has been studied, and two methods for counteracting drift are proposed. One is based on reference patterns that are updated when new measurements are made, and the other uses models known from system identification theory. The two methods are suited for different experimental situations, and both give a better result than a back-propagation neural network classifier when they are used under the right conditions.