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

doi:10.1109/TIE.2005.862212

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 journal › Article › peer-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 language	English
Pages (from-to)	313-321
Number of pages	9
Journal	IEEE Transactions on Industrial Electronics
Volume	53
Issue number	1
DOIs	https://doi.org/10.1109/TIE.2005.862212
Publication status	Published - Feb 2006

Keywords

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

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10.1109/TIE.2005.862212

Cite this

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

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.",

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AU - Kawamoto, Kazuhiko

AU - Hirota, Kaoru

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AB - 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.

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

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Keywords

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