Application of an artificial neural network in the processing of output signals from a gas sensor with sol-gel-derived TiO2 film

Abstract

TiO2 thin film obtained by the sol-gel technique was used as the active layer in an electric sensor to distinguish the vapours of four volatile organic compounds: hexane, hexanol, cyclohexane and benzene. The measurements were performed at various temperatures of the sensing layer. Some of the output signals obtained from the sensor were characterized by low reproducibility, even within the data series obtained for the same gas. With the current design of the gas sensor, it was sometimes impossible to obtain a reproducible and stable output signal. Therefore, a neural network was used to pre-process the data. A bipolar transfer function of neurons was used as it had the shortest learning time of the network and produced the most stable results. The best results were obtained for a 4-4-4 topology of the neural network, where the input data were the values of the current at 440 and 360 °C when the sensor was exposed to a flow of air with or without organic vapours, with a 4-neuron hidden layer, and BE, CH, HL, HX outputs, each one associated with specific substance (benzene, cyclohexane, hexanol and hexane). The neural network was configured as a classifier recognizing four specific gases.

6 Figures and Tables

Cite this paper

@inproceedings{ukowiak2007ApplicationOA, title={Application of an artificial neural network in the processing of output signals from a gas sensor with sol-gel-derived TiO2 film}, author={Anna Łukowiak and Katarzyna Kozłowska and Konrad Urbanski and Kay Dudek and Krzysztof Maruszewski}, year={2007} }