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

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.