Abstract
We consider an array sensing system of odors and adopt a layered neural network for classification. Measurement data obtained from fourteen metal oxide semiconductor gas (MOG) sensors are used, where some sensors exhibit relatively weak responses.We propose two methods for enhancing such weak signals to obtain better classification results. One method is to apply scaling to magnify the weak signals as to increase their significance in the classification criteria. The other method also involves magnifying the weak signals. However, predetermined values are assigned in the order of the magnitude of the actual signals. In both methods the group of weak signals is first determined. Then their values are negated prior to scaling, in order to be distinguished from stronger signals. An experiment shows that the accuracy of classifying five kinds of odors is improved from 74% to 85%.
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Omatu, S., Yano, M., Fujinaka, T. (2013). Odor Classification Based on Weakly Responding Sensors. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_15
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DOI: https://doi.org/10.1007/978-3-319-00551-5_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
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