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)00502-5KeyWords: olfaction; adaptive resonance theory; fuzzy logic; parameter drift; DOI: 10.1016/S0925-4005(00)00502-5ImpactFactor: 1.470Citations: 25data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-10References taken from IsiWeb of Knowledge: (subscribers only)Connecting to view paper tab on IsiWeb: Click hereConnecting to view citations from IsiWeb: Click here