Fuzzy neural networks for gas sensing

Vlachos, Dimitrios; Avaritsiotis, John

dc.contributor.author	Vlachos, Dimitrios	en
dc.contributor.author	Avaritsiotis, John	en
dc.date.accessioned	2014-03-01T02:41:05Z
dc.date.available	2014-03-01T02:41:05Z
dc.date.issued	1995	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30360
dc.subject	Fuzzy Neural Network	en
dc.subject.other	Atmospheric humidity	en
dc.subject.other	Boolean functions	en
dc.subject.other	Carbon monoxide	en
dc.subject.other	Ethanol	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Methane	en
dc.subject.other	Neural networks	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Tin compounds	en
dc.subject.other	Fuzzy calculus	en
dc.subject.other	Multicomponent analysis	en
dc.subject.other	Tin oxide based gas sensor	en
dc.subject.other	Chemical sensors	en
dc.title	Fuzzy neural networks for gas sensing	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/SENSOR.1995.717328	en
heal.identifier.secondary	http://dx.doi.org/10.1109/SENSOR.1995.717328	en
heal.publicationDate	1995	en
heal.abstract	The implementation of a fuzzy neural network with an array of tin oxide based gas sensors for both quantitative and qualitative gas sensing is demonstrated. The architecture of the system is presented with some references to the general theory of fuzzy sets and fuzzy calculus. Experimental results are presented in the case of gas identification between CO, ethanol and methane and in the case of CO detection in different levels of relative humidity. Finally the effect of network parameters to the functionally of the system is discussed, especially in the case of functions evaluating the fuzzy AND and OR operations.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	International Conference on Solid-State Sensors and Actuators, and Eurosensors IX, Proceedings	en
dc.identifier.doi	10.1109/SENSOR.1995.717328	en
dc.identifier.volume	1	en
dc.identifier.spage	703	en
dc.identifier.epage	706	en