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Sensors 2009, 9(11), 8944-8960; doi:10.3390/s91108944
Article

A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment

*  and
Received: 9 September 2009; in revised form: 27 October 2009 / Accepted: 30 October 2009 / Published: 11 November 2009
(This article belongs to the Special Issue Gas Sensors 2009)
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Abstract: Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX) are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…). We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor’s response characteristics and eliminate its dependency on the environmental parameters. The corrector’s responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration). The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering). The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor’s responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.
Keywords: gas sensor; ANN; implementation; ABM; corrector gas sensor; ANN; implementation; ABM; corrector
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Baha, H.; Dibi, Z. A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment. Sensors 2009, 9, 8944-8960.

AMA Style

Baha H, Dibi Z. A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment. Sensors. 2009; 9(11):8944-8960.

Chicago/Turabian Style

Baha, Hakim; Dibi, Zohir. 2009. "A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment." Sensors 9, no. 11: 8944-8960.



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