A fuzzy-similarity-based self-organized network inspired by immune algorithm for three-mixture-fragrance recognition
Widyanto, M.R.; Kusumoputro, B.; Nobuhara, H.; Kawamoto, K.; Hirota, K.
Industrial Electronics, IEEE Transactions on
Volume 53, Issue 1, Feb. 2006 Page(s): 313 - 321
Digital Object Identifier   10.1109/TIE.2005.862212
Summary: 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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