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

 
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1997 (EN)
An adaptive neural network topology for degradation compensation of thin film tin oxide gas sensors (EN)

Avaritsiotis, JN (EN)
Fragoulis, DK (EN)
Vlachos, DS (EN)

N/A (EN)

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)

journalArticle

Degradation (EN)
Neural networks (EN)
Electrochemical sensors (EN)
Electric network topology (EN)
Oxides (EN)
Pattern recognition (EN)
Gas sensors (EN)
Thin film tin oxide gas sensors (EN)
Neural network topology (EN)

Εθνικό Μετσόβιο Πολυτεχνείο (EL)
National Technical University of Athens (EN)

Sensors and Actuators, B: Chemical (EN)

1997


ELSEVIER SCIENCE SA (EN)



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