A fully neural implementation of unitary response model for classification of gases/odors using the responses of thick film gas sensor array

In this paper, a classification scheme based on neurally implemented unitary response model (URM) for a gas/odor sensor array response has been presented. Thick-film tin-oxide sensor array responses for four gases/odors (viz. acetone, carbon tetra-chloride, ethyl methyl ketone and xylene) were first transformed into equivalent unitary responses. This transformation was carried out using a pre-trained neural ‘unitary response model pre-processor (URMP)’, called Net IURMP. The classification of these responses in the unitary analysis space was then carried out, more accurately, using a pre-trained neural classifier called Net IIURMC. During this experiment, respective nets Net IURMP and Net IIURMC, comprising of 12 and 8 neurons, were trained in just 23 and 09 epochs of 42 × 4 training response vectors. At stage I, the mean squared error (MSE) between neurally and mathematically obtained unitary response versions of 18 independent test responses for the considered gases/odors was 7.51 × 10−2. At stage II, all the aforesaid test samples were correctly classified, with a MSE of 3.87 × 10−8. Further, by connecting Net IURMP and Net IIURMC in cascade, the proposed classifier could be implemented using 16 neurons only.