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A gas sensor array for environmental air monitoring: A study case of application of artificial neural networks

TitoloA gas sensor array for environmental air monitoring: A study case of application of artificial neural networks
Tipo di pubblicazionePresentazione a Congresso
Anno di Pubblicazione2011
AutoriPenza, Michele, Suriano Domenico, Cassano Gennaro, Rossi R., Alvisi Marco, Pfister Valerio, Trizio L., Brattoli M., and De Gennaro G.
Conference NameAIP Conference Proceedings
Conference LocationNew York City, NY
ISBN Number9780735409200
Abstract

An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air-pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal-modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi-layer feed-forward network based on error back-propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron-based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2 characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (NMSE) in the test set as low as 6%. © 2011 American Institute of Physics.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-80053274544&doi=10.1063%2f1.3626360&partnerID=40&md5=74aa9ae1245b00ae01865bfcfeaf917c
DOI10.1063/1.3626360
Citation KeyPenza2011205