Neural Network Technique for Electronic Nose Based on High Sensitivity Sensors Array
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Electronic Nose, as an artificial olfaction system, has potential applications in environmental monitoring because of its proven ability to recognize and discriminate between a variety of different gases and odors. In this paper, we used a chemical sensor array to develop an electronic nose to detect and identify seven different gases (H2, C2H2, CH4, CH3OCH3, CO, NO2, and NH3). These gas sensors are chosen because of its hierarchical/doped nanostructure characteristics, which give them a very high sensitivity and low response time; we improve the linearity response and temperature dependence using models based on artificial neural networks. We used in Electronic nose a pattern recognition based on artificial neural network, which discriminates qualitatively and quantitatively seven gases and has a fast response.
KeywordsElectronic nose E-nose Gas sensor ANN High sensitivity Fast response
- 1.Hwang, S., Kwon, H., Chhajed, S., Byon, J. W., Baik, J. M., Im, J., et al. (2013). A near single crystalline TiO2 nanohelix array: enhanced gas sensing performance and its application as a monolithically integrated electronic nose. Analyst, 138, 443. https://doi.org/10.1039/c2an35932d.CrossRefGoogle Scholar
- 3.Khaldi, S., & Dibi, Z. (2016). ANN modeling of electronic nose based on co-doped SnO2 nanofiber sensor. Sensors & Transducers, 200(5), 24–28.Google Scholar
- 10.Zamanisabzi, H., King, J. P., Dilekli, N., Shoghli, B., & Abudu, S. (2018). Developing an ANN based streamflow forecast model utilizing data-mining techniques to improve reservoir streamflow prediction accuracy: A case study. Civil Engineering Journal, 4(5), 1135–1156. https://doi.org/10.28991/cej-0309163.CrossRefGoogle Scholar
- 12.Khaldi, S., & Dibi, Z. (2017). Neural network modeling of smart nanostructure sensor for electronic nose application. In Proceedings of the 6th international conference on systems and control (ICSC), University of Batna 2, Batna, Algeria, May 7–9, 2017. https://doi.org/10.1109/icosc.2017.7958690.