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Mixed Odor Classification for QCM Sensor Data by Neural Network

Author(s): Sigeru OMATU | Hideo ARAKI | Toru FUJINAKA | Mitsuaki YANO | Michifumi YOSHIOKA | Hiroyuki NAKAZUMI | Ichiro TANAHASHI

Journal: ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal
ISSN 2255-2863

Volume: 1;
Issue: 2;
Start page: 43;
Date: 2013;
Original page

Keywords: Odor feature vector | Neural networks | Separation of mixed gases | Odor classification

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 before mixing 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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