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

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5 Author(s)
M. R. Widyanto ; Dept. of Comput. Intelligence & Syst. Sci., Tokyo Inst. of Technol., Yokohama, Japan ; B. Kusumoputro ; H. Nobuhara ; K. Kawamoto
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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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IEEE Transactions on Industrial Electronics  (Volume:53 ,  Issue: 1 )