On-line Calibration of Semiconductor Gas Sensors Based on Prediction Model

Abstract

In this study, an Electronic nose (Enose) instrument used indoor for monitoring formaldehyde is designed. In mass production of this instrument, because of the inherent variability in the sensor manufacturing process, the Enose instruments give different outputs. It is impossible to train an individual prediction model on each instrument to have uniform output. A new on-line calibration method based on prediction model without real master instrument is proposed. This method avoids the problem that if the real master instrument behaves drift, the calibration of the other batch of instruments would lose its effect. In this paper, the prediction model is radial basis function (RBF) neural network and particle swarm optimization (PSO) is used to determine the parameters in RBF. The results show that the responses of the same type sensors are uniform after calibration, and this new method is easy and robust.