Odor discrimination using adaptive resonance theory

Year: 2000

Authors: Distante C., Siciliano P., Vasanelli L.

Autors Affiliation: Dipartimento di Ingegneria dell’Innovazione, Universita’ di Lecce, via Monteroni 73100 Lecce, Italy; IME-CNR, via Monteroni 73100 Lecce, Italy

Abstract: The paper presents two neural networks based on the adaptive resonance theory (ART) for the recognition of several odors subjected to drift. The neural networks developed by Grossberg (supervised and unsupervised) have been used for two different drift behaviors. One in which the clusters end up to overlap each other and the other when they do not. The latter case is solved by unsupervision, which is useful to track the moving clusters and possibly discover new odors autonomously. (C) 2000 Elsevier Science S.A. All rights reserved.

Journal/Review: SENSORS AND ACTUATORS B-CHEMICAL

Volume: 69 (3)      Pages from: 248  to: 252

More Information: doi: 10.1016/S0925-4005(00)00502-5
KeyWords: olfaction; adaptive resonance theory; fuzzy logic; parameter drift;
DOI: 10.1016/S0925-4005(00)00502-5

ImpactFactor: 1.470
Citations: 25
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