QUANTIFICATION OF H2S AND NO2 USING GAS SENSOR ARRAYS AND AN ARTIFICIAL NEURAL-NETWORK

Based on a dedicated sensor array and a novel data pre-processing func tion, we report the successful application of a multilayer perceptron to a sensor array to give quantitative identification of individual ga s concentrations (H2S and NO2) in their gas mixtures. The sensors were produced by the rheotaxial growth and thermal oxidation technique. Ou r raw sensor's responses covered a range of four magnitudes due to the different responses of the sensors. By comparing several pre-processi ng methods, we demonstrated that pre-processing of input data has cruc ial influence on the final performance of neural networks. Our adoptio n of the pre-processing rule might be of general usefulness for the ca se of a large range of raw data. (C) 1997 Elsevier Science S.A.