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 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 analize 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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© 2012 Springer-Verlag Berlin Heidelberg
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Omatu, S., Araki, H., Fujinaka, T., Yoshioka, M., Nakazumi, H. (2012). Mixed Odor Classification for QCM Sensor Data by Neural Networks. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., RodrÃguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_1
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DOI: https://doi.org/10.1007/978-3-642-28765-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28764-0
Online ISBN: 978-3-642-28765-7
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