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