Improvement of artificial odor discrimination system using fuzzy-LVQ neural network

Benyamin Kusumo Putro, Muhammad Rahmat Widyanto, Mohamad Ivan Fanany, H. Budiarto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

An artificial odor recognition system is developed in order to mimic the human sensory test in cosmetics, perfume and beverage industries. A backpropagation neural network is used as the pattern recognition system and shows high recognition capability. However, the system only works efficiently when it is used to discriminate a limited number of odors. The unlearned odor will be classified as one of the already learned category. To improve the system's capability, a fuzzy learning vector quantization neural network is developed and utilized in experiments on four different ethanol concentrations, and three different kinds of fragrance odor from Martha Tilaar Cosmetics. The results shows that the FLVQ has a comparable ability for recognizing the already known category of odors. However, the FLVQ algorithm can cluster the unknown odor in a different new class of odor.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages474-478
Number of pages5
ISBN (Electronic)0769503004, 9780769503004
DOIs
Publication statusPublished - 1999
Event3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999 - New Delhi, India
Duration: 23 Sept 199926 Sept 1999

Publication series

NameProceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999

Conference

Conference3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999
Country/TerritoryIndia
CityNew Delhi
Period23/09/9926/09/99

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