An adaptive neural network topology for degradation compensation of thin film tin oxide gas sensors

Vlachos, DS; Fragoulis, DK; Avaritsiotis, JN

dc.contributor.author	Vlachos, DS	en
dc.contributor.author	Fragoulis, DK	en
dc.contributor.author	Avaritsiotis, JN	en
dc.date.accessioned	2014-03-01T01:12:36Z
dc.date.available	2014-03-01T01:12:36Z
dc.date.issued	1997	en
dc.identifier.issn	0925-4005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12162
dc.subject	Gas sensors	en
dc.subject	Neural networks	en
dc.subject.classification	Chemistry, Analytical	en
dc.subject.classification	Electrochemistry	en
dc.subject.classification	Instruments & Instrumentation	en
dc.subject.other	Degradation	en
dc.subject.other	Electric network topology	en
dc.subject.other	Neural networks	en
dc.subject.other	Oxides	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Neural network topology	en
dc.subject.other	Thin film tin oxide gas sensors	en
dc.subject.other	Electrochemical sensors	en
dc.title	An adaptive neural network topology for degradation compensation of thin film tin oxide gas sensors	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0925-4005(97)00309-2	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0925-4005(97)00309-2	en
heal.language	English	en
heal.publicationDate	1997	en
heal.abstract	A hybrid neural network for gas sensing application is presented, which is based on adaptive resonance theory. The network may use as an input one or more gas sensors. The basic feature of the proposed topology is its ability to learn a new pattern or form a new pattern category at any point of its operation. At the same time it retains knowledge of previously learned patterns or pattern categories. This adaptation ability helps the network to solve many of the problems encountered with tin oxide gas sensors, like instabilities and degradation. The functionality of the network is presented in the two cases of one and four input providing gas sensors. The experimental results show that the effect of sensor degradation maybe compensated by the proposed network topology. (C) 1997 Elsevier Science S.A. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Sensors and Actuators, B: Chemical	en
dc.identifier.doi	10.1016/S0925-4005(97)00309-2	en
dc.identifier.isi	ISI:000072641900007	en
dc.identifier.volume	45	en
dc.identifier.issue	3	en
dc.identifier.spage	223	en
dc.identifier.epage	228	en