An optimized deep convolutional neural network for dendrobium classification based on electronic nose

被引：55

作者：

Wang, You ^{[1
]}

Diao, Junwei ^{[1
]}

Wang, Zhan ^{[1
]}

Zhan, Xianghao ^{[1
,3
]}

Zhang, Bixuan ^{[1
]}

Li, Nan ^{[2
]}

Li, Guang ^{[1
]}

机构：

[1] Zhejiang Univ, Inst Cyber Syst & Control, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China

[2] Cambridge Univ West Site, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr, Cambridge CB3 0AS, England

[3] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA

来源：

SENSORS AND ACTUATORS A-PHYSICAL | 2020年 / 307卷 / 307期

关键词：

Deep convolutional neural network; Electronic nose; Classification; Dendrobium; FEATURE-SELECTION; RECOGNITION;

D O I：

10.1016/j.sna.2020.111874

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces an optimized deep convolutional neural network (DCNN) using special banded 1D kernels at the convolutional and the pooling layers adapted for electronic nose (E-nose) data. It is used to classify multiple types of Chinese herbal medicine. The optimized DCNN network is composed of 5 special convolutional layers with 1D convolutional kernels, 2 special pooling layers with 1D size, 1 fully connected layer and 1 Softmax layer. Results show that the optimized DCNN achieves the best accuracy of 87.56%, outperforming the 81.67% from the second-best classifier DCNN. The optimized DCNN extracts features from E-nose data faster and better than common DCNN. This paper also proposes an insight of applying DCNN to small-scale and E-nose data. (C) 2020 Published by Elsevier B.V.

引用

页数：9

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