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 inflamma ble gases from transient response patterns which a semiconductor gas s ensor shows under periodic heating conditions. The procedure and effec tiveness of the method were illustrated for five selected gases of but ane, hydrogen, LP gas, methane, and town gas. The response patterns ob tained were found to be well reproducible and specific to the kinds of gases. Frequency analysis could be applied easily to the response pat terns because of their periodic characteristics, allowing one to extra ct 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 discrimina tion of gases. The D.C. components, on the other hand, depended largel y on the Variations of gas concentration, being useful for the estimat ion of gas concentration. It was shown that the discrimination of the five gases supported by a three-layered back propagation neural networ k as well as the estimation of their concentrations assisted by fuzzy inference were successfully performed. (C) 1997 Elsevier Science S.A.