Self-organized network with a supervised training and its comparison with FALVQ in artificial odor recognition system

Benyamin Kusumoputro; Linda Rostiviani; Ari Saptawijaya

doi:10.1117/12.394070

28 July 2000 Self-organized network with a supervised training and its comparison with FALVQ in artificial odor recognition system

Benyamin Kusumoputro, Linda Rostiviani, Ari Saptawijaya

Author Affiliations +

Proceedings Volume 4036, Chemical and Biological Sensing; (2000) https://doi.org/10.1117/12.394070
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Artificial odor recognition system is developed in order to mimic the human sensory test in cosmetics, parfum and beverage industries. The developed system however, lacks of ability to recognize the unknown type of odor. To improve the system's capability, a hybrid neural system with a supervised learning paradigm is developed and used as a pattern classifier. In this paper, the performance of the hybrid neural system is investigated, together with that of FALVQ neural system.

Citation Download Citation

Benyamin Kusumoputro, Linda Rostiviani, and Ari Saptawijaya "Self-organized network with a supervised training and its comparison with FALVQ in artificial odor recognition system", Proc. SPIE 4036, Chemical and Biological Sensing, (28 July 2000); https://doi.org/10.1117/12.394070

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