An initialization scheme of fuzzy-neuro LVQ for discriminating three-mixtures odor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

There are still major difficulties in the usage of fuzzy neural networks based on LVQ (FNLVQ) algorithms, i.e choosing the initialization of the fuzzy-reference vectors. The initialization step is important due to different selections of the initial reference vectors may potentially lead to different partition for different classes, which hampered the superiority of the algorithm. In this paper, we proposed a novel initialization method, by transforming all data from the origin problem space into its eigen space prior the usage of FNLVQ. Experiments are conducted in an artificial odor recognition system and it shows that the performance of FNLVQ in eigen space has higher recognition rate compare with that of in aroma space, especially for 18 class of three-mixture odors problem.

Original languageEnglish
Title of host publicationProceedings of the 5th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2008
Pages232-237
Number of pages6
Publication statusPublished - 1 Dec 2008
Event5th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2008 - Innsbruck, Austria
Duration: 13 Feb 200815 Feb 2008

Publication series

NameProceedings of the 5th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2008

Conference

Conference5th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2008
CountryAustria
CityInnsbruck
Period13/02/0815/02/08

Keywords

  • Artificial odor recognition system
  • Eigenspace
  • Fuzzy-LVQ
  • Quartz micro balance

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