Feature-level signal processing for odor sensor arrays

Abstract

A recurrent back-propagation neural algorithm is trained to classify nine odors. The algorithm is capable of correctly identifying the odors regardless of the time sequence of presentation. The classification is performed in near-real time and is based upon the transient response of an array of 15 tin-oxide gas sensors.