THE DISCRIMINATION OF MANY KINDS OF ODOR SPECIES USING FUZZY-REASONING AND NEURAL NETWORKS

To discriminate many kinds of odor species, a system composed of multiple gas sensors and neural networks is proposed. Three commercial gas sensors are used for the system, and four kinds of inflammable gases, four kinds of fragrant smells and one kind of offensive odor are introduced as odor species. The discrimination is performed in two steps to increase the efficiency of the system; the first step is classification of the odor group, that is, the groups of inflammable gases, fragrant smells and offensive odor; the second step is the discrimination of individual odor species in the classified group. 100% group classification rate is obtained by the use of simple fuzzy reasoning and the steady-state response patterns of the sensors. The discrimination of individual odor species is performed with a neural network and transient response patterns of the sensors and a high discrimination rate (99.2%) is achieved.