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Title Design of a Selective Smart Gas Sensor Based on ANN-FL Hybrid Modeling
Authors Kouda, S.
Dendouga, A.
Barra, S.
Bendib, T.
ORCID
Keywords fuzzy logic
artificial neural networks
gas sensor
selectivity
analytical model
selective model
Type Article
Date of Issue 2018
URI http://essuir.sumdu.edu.ua/handle/123456789/71607
Publisher Sumy State University
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Citation Design of a Selective Smart Gas Sensor Based on ANN-FL Hybrid Modeling / S. Kouda, A. Dendouga, S. Barra, T. Bendib // Журнал нано- та електронної фізики. - 2018. - Т.10, № 6. - 06011. - DOI: 10.21272/jnep.10(6).06011
Abstract The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.
Appears in Collections: Журнал нано- та електронної фізики (Journal of nano- and electronic physics)

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