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

Muhammad Rahmat Widyanto, Benyamin Kusumo Putro, Hajime Nobuhara, Kazuhiko Kawamoto, Kaoru Hirota

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)313-321
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Volume53
Issue number1
DOIs
Publication statusPublished - 1 Feb 2006

Keywords

  • Artificial odor discrimination
  • Fuzzy similarity
  • Immune algorithm
  • Self-organized network
  • Three-mixture-fragrance problem

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