[1]ZHOU Hongbiao,ZHANG Yulin,DING Youwei,et al.Application of adaptive probabilistic neural network in Chinese liquor E-Nose[J].CAAI Transactions on Intelligent Systems,2013,8(2):177-182.[doi:10.3969/j.issn.1673-4785.201209026]
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Application of adaptive probabilistic neural network in Chinese liquor E-Nose

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 2 Page number: 177-182 Column: 学术论文—机器学习 Public date: 2013-04-25
Title:
Application of adaptive probabilistic neural network in Chinese liquor E-Nose
Author(s):
ZHOU Hongbiao1;  ZHANG Yulin1;  DING Youwei1;  LIU Jiajia2
1.Faculty of Electronic and Electrical Engineering, Huaiyin Institute of Technology, Huai’an 223003, China;
2.School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210042, China
Keywords:
differential evolution algorithm; adaptive probabilistic neural network; electronic nose; hard liquor quality recognition
CLC:
TP183；TS262.3
DOI:
10.3969/j.issn.1673-4785.201209026
Abstract:
In order to explore the possibility of hard liquor quality recognition by an electronic nose, the Chinese liquor of Yanghe Haizhilan, Jinshiyuan Shengjiedai, Anhui Yingjiadaqu, and Niulanshan Chenniang were analyzed by using self-made new wireless electronic nose for recognition of hard liquor quality. Firstly, the steady-state response and slope values were extracted after smoothing the collected data. Secondly, principal component analysis PCA was used to reduce the dimension of the eigenvector, and the obtained first two principal components scores were then used as the input parameters of the probabilistic neural network recognition model. Next, the aim was to overcome defect of traditional probabilistic neural network smoothing factor which would cause classification error easily. The method of adaptive probabilistic neural network identification model was presented, utilizing differential evolution algorithm to optimize the set of parameters. The results show that differential evolution-probabilistic neural network obtained a high recognition accuracy and noise immunity compared to back propagation, particle swarm optimization-probabilistic neural network and support vector machine. The experiment also proved that the electronic nose can effectively detect different liquor brands in China.
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