Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses
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 ar...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/19/1/217 |
| _version_ | 1811308349579329536 |
|---|---|
| author | Guangfen Wei Gang Li Jie Zhao Aixiang He |
| author_facet | Guangfen Wei Gang Li Jie Zhao Aixiang He |
| author_sort | Guangfen Wei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:21:37Z |
| format | Article |
| id | doaj.art-e5c6486858594c339773bf60e5fcd903 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:21:37Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e5c6486858594c339773bf60e5fcd9032022-12-22T02:52:35ZengMDPI AGSensors1424-82202019-01-0119121710.3390/s19010217s19010217Development of a LeNet-5 Gas Identification CNN Structure for Electronic NosesGuangfen Wei0Gang Li1Jie Zhao2Aixiang He3School of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, ChinaSchool of Computer Science & Technology, Shandong Technology and Business University, Yantai 264005, ChinaSchool of Computer Science & Technology, Shandong Technology and Business University, Yantai 264005, ChinaSchool of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, ChinaA 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.http://www.mdpi.com/1424-8220/19/1/217gas identificationelectronic nosepattern recognitionconvolutional neural network |
| spellingShingle | Guangfen Wei Gang Li Jie Zhao Aixiang He Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses Sensors gas identification electronic nose pattern recognition convolutional neural network |
| title | Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses |
| title_full | Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses |
| title_fullStr | Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses |
| title_full_unstemmed | Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses |
| title_short | Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses |
| title_sort | development of a lenet 5 gas identification cnn structure for electronic noses |
| topic | gas identification electronic nose pattern recognition convolutional neural network |
| url | http://www.mdpi.com/1424-8220/19/1/217 |
| work_keys_str_mv | AT guangfenwei developmentofalenet5gasidentificationcnnstructureforelectronicnoses AT gangli developmentofalenet5gasidentificationcnnstructureforelectronicnoses AT jiezhao developmentofalenet5gasidentificationcnnstructureforelectronicnoses AT aixianghe developmentofalenet5gasidentificationcnnstructureforelectronicnoses |