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A fuzzy-similarity-based self-organized network inspired by immune algorithm for three-mixture-fragrance recognition

  • Muhammad R. Widyanto*
  • , Benyamin Kusumoputro
  • , Hajime Nobuhara
  • , Kazuhiko Kawamoto
  • , Kaoru Hirota
  • *此作品的通讯作者
  • Tokyo Institute of Technology
  • University of Indonesia

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

A fuzzy-similarity-based self-organized network inspired by immune algorithm (F-SONIA) is proposed in order to develop an artificial odor discrimination system for three-mixture-fragrance recognition. It can deal with an uncertainty in frequency measurements, which is inherent in odor acquisition devices, by employing a fuzzy similarity. Mathematical analysis shows that the use of the fuzzy similarity results on a higher dissimilarity between fragrance classes, therefore, the recognition accuracy is improved and the learning time is reduced. Experiments show that F-SONIA improves recognition accuracy of SONIA by 3% - 9% and the previously developed artificial odor discrimination system by 14% - 25%. In addition, the learning time of F-SONIA is three times faster than that of SONIA.

源语言英语
页(从-至)313-321
页数9
期刊IEEE Transactions on Industrial Electronics
卷53
期1
DOI
出版状态已出版 - 2月 2006
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