The concentration-estimation of inflammable gases with a semiconductor gas sensor utilizing neural networks and fuzzy inference

This paper proposes a method to estimate the concentration of inflammable gases from transient response patterns which a semiconductor gas sensor shows under periodic heating conditions. The procedure and effectiveness of the method were illustrated for five selected gases of butane, hydrogen, LP gas, methane, and town gas. The response patterns obtained were found to be well reproducible and specific to the kinds of gases. Frequency analysis could be applied easily to the response patterns because of their periodic characteristics, allowing one to extract D.C. and A.C. components of them by fast Fourier transform. The A.C. components remained almost unchanged irrespective of the variations of ambient temperature and/or humidity and gas concentration, proving themselves to be adequate for the concentration-independent discrimination of gases. The D.C. components, on the other hand, depended largely on the Variations of gas concentration, being useful for the estimation of gas concentration. It was shown that the discrimination of the five gases supported by a three-layered back propagation neural network as well as the estimation of their concentrations assisted by fuzzy inference were successfully performed. (C) 1997 Elsevier Science S.A.