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Journal of Bionic Engineering

, Volume 5, Issue 3, pp 253–257 | Cite as

Comparison of Algorithms for an Electronic Nose in Identifying Liquors

  • Zhi-biao ShiEmail author
  • Tao Yu
  • Qun Zhao
  • Yang Li
  • Yu-bin Lan
Article

Abstract

When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, the different brands of distilled spirits were identified using the pattern recognition algorithms (principal component analysis and the artificial neural network). The recognition rates of different algorithms were compared. The recognition rate of the Back Propagation Neural Network (BPNN) is the highest. Owing to the slow convergence speed of the BPNN, it tends easily to get into a local minimum. A chaotic BPNN was tried in order to overcome the disadvantage of the BPNN. The convergence speed of the chaotic BPNN is 75.5 times faster than that of the BPNN.

Keywords

electronic nose liquor algorithm principal component analysis 

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Copyright information

© Jilin University 2008

Authors and Affiliations

  • Zhi-biao Shi
    • 1
    Email author
  • Tao Yu
    • 2
  • Qun Zhao
    • 1
  • Yang Li
    • 3
  • Yu-bin Lan
    • 4
  1. 1.School of Energy Resources and Mechanical EngineeringNortheast Dianli UniversityJilinP. R. China
  2. 2.School of Chemistry EngineeringNortheast Dianli UniversityJilinP. R. China
  3. 3.School of Electrical EngineeringNortheast Dianli UniversityJilinP. R. China
  4. 4.Aerial Application TechnologyUSDA-ARS-SPARC-APMRUCollege StationUSA

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