Design of a Selective Smart Gas Sensor Based on ANN-FL Hybrid Modeling

Authors S. Kouda1, A. Dendouga2, 3 , S. Barra2, T. Bendib1
Affiliations

1University of M’SILA, Faculty of Technology, 28000 M’sila, Algeria

2Laboratoire d’Electronique Avancée-LEA, 05000 Batna, Algeria

3University of BATNA 2, Faculty of Technology, 05000 Batna, Algeria

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Issue Volume 10, Year 2018, Number 6
Dates Received 15 July 2018; revised manuscript received 01 December 2018; published online 18 December 2018
Citation S. Kouda, A. Dendouga, S. Barra, T. Bendib, J. Nano- Electron. Phys. 10 No 6, 06011 (2018)
DOI https://doi.org/10.21272/jnep.10(6).06011
PACS Number(s) 07.07.Df, 44.05. + e
Keywords Fuzzy logic, Artificial neural networks, Gas sensor (5) , Selectivity (2) , Analytical model (3) , Selective model.
Annotation

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.

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