Cross sensitivity reduction of gas sensors using genetic neural network

Abstract

The IR absorption method is a traditional spectral analysis method in the analysis of gas components. Nevertheless, the absorption spectrum lines of a certain kind of gas often intercross with those of another, which means that the absorption peak of one kind of gas is close to that of another. Thus, when these kinds of gases are mixed together, there will be cross-sensitivity in the concentration measurement using the gas sensor system. We adopt the genetic neural network algorithm to recognize the patterns of the mixed gas with two components and three components respectively in the simulation recognition. The staged-sectional recognition method is used to increase the recognition precision at various critical values of the mixed gases. The simulation results indicate that the staged-sectional method based on the genetic neural networks decreases the cross-sensitivity of the gas sensor and increases the recognition precision at various critical values.