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Sensors 2019, 19(1), 217; https://doi.org/10.3390/s19010217

Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses

1,2,*, 3, 3 and 1,2
1
School of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China
2
Key Laboratory of Sensing Technology and Control in Universities of Shandong, Shandong Technology and Business University, Yantai 264005, China
3
School of Computer Science & Technology, Shandong Technology and Business University, Yantai 264005, China
*
Author to whom correspondence should be addressed.
Received: 19 November 2018 / Revised: 24 December 2018 / Accepted: 4 January 2019 / Published: 8 January 2019
(This article belongs to the Section Intelligent Sensors)
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Abstract

A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH4 and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH4 and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach. View Full-Text
Keywords: gas identification; electronic nose; pattern recognition; convolutional neural network gas identification; electronic nose; pattern recognition; convolutional neural network
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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 (CC BY 4.0).
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Wei, G.; Li, G.; Zhao, J.; He, A. Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses. Sensors 2019, 19, 217.

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