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Sigeru Omatu
Osaka Institute of Technology
Japan
Hideo Araki
Osaka Institute of Technology
Japan
Toru Fujinaka
Hirosima University
Japan
Mitsuaki Yano
Osaka Institute of Technology
Japan
Michifumi Yoshioka
Osaka Prefecture University
Japan
Hiroyuki Nakazumi
Osaka Prefecture Univertisy
Japan
Ichiro Tanahashi
Osaka Institute of Technology
Japan
Vol. 1 No. 2 (2012), Articles, pages 43-48
DOI: https://doi.org/10.14201/ADCAIJ2012124348
Accepted: Jul 1, 2013
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Abstract

Compared with metal oxide semiconductor gas sensors, quarts crystal microbalance (QCM) sensors are sensitive for odors. Using an array of QCM sensors, we measure mixed odors and classify them into an original odor class beforemixing based on neural networks. For simplicity we consider the case that two kinds of odor are mixed since more than two becomes too complex to analyze the classification results. We have used eight sensors and four kinds of odor are used as the original odors. The neural network used here is a conventional layered neural network. The classification is acceptable although the perfect classification could not been achieved.

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References

MILKE , John. Application of Neural Networks for discriminating Fire Detectors, International Conference on Automatic Fire Detection, AUBE’95, 10th, Duisburg, 1995. Germany

CHARUMPORN, Bancha. An Electronic Nose System Using Back Propagation Neural Networks with a Centroid Training Data Set, Proc.

Eighth International Symposium on Artificial Life and Robotics, 2003. Japan

FUJINAKA, Toru, Intelligent Electronic Nose Systems for Fire Detection Systems Based on Neural Networks, The second International Conference on Advanced Engineering Computing and Applications in Sciences, 2008. Spain

OMATU, Sigeru, Intelligent Electronic Nose System Independent on Odor

Concentration, International Symposium on Distributed Computing and Artificial Intelligence, 2011. Spain