Abstract
The sensitivity of a catalytic sensor changes according to different types of gases or different temperatures. To fully exploit this property, a sensor can be controlled to work under different temperatures to produce different output signals for a given mixture of inflammable gases. An Radial Basis Function(RBF) neural network can be used to analyze the mixture of gases by using a dynamic learning algorithm. The simulation experiment, with a sample mixture of firedamp, carbon monoxide and hydrogen, shows that the proposed method is indeed efficient in analyzing mixtures of inflammable gases.
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© 2009 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Qi, M., Gu, C. (2009). Analysis of Mixed Inflammable Gases Based on Single Sensor and RBF Neural Network. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_92
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DOI: https://doi.org/10.1007/978-3-642-04020-7_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
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