An odor discrimination approach based on mice olfactory neural network

Abstract

Characteristic signals of the novelty volatile odor shed by equipments at abnormal state are often with higher dimension, and difficult to discriminate because of the complex background odorant noise in non-open space. An artificial olfactory neural network and its learning algorithm are introduced based on the anatomy of odor discrimination mechanism and olfactory neural model of mice. After the construction and training of an olfactory neural network for the discrimination of kerosene, gear oil and alcohol, it is verified through experiment data sets. The results indicate that the artificial neural network (ANN) based on mice olfactory model achieves a short time for training and the identification rate is feasible and effective. © 2012 Springer-Verlag GmbH.