An array of semiconducting thin-film sensors is used for high-accuracy determination of gas concentrations in a binary mixture of H2S and NO2. For this purpose we developed a newly conceived pre-processing method for data feeding of a standard neural network. Pre-processing exploits dimension compaction of the vectorial signal of the sensor array. This operation is being accomplished by an auxiliary auto-associative network, separately trained from the main one. Our results present unprecedented accuracy even when applied to a validation-data set and appear to be of widespread application.

High-precision neural preprocessing for signal analysis of a sensor array

GNANI, Dario;GUIDI, Vincenzo;FERRONI, Matteo;
1998

Abstract

An array of semiconducting thin-film sensors is used for high-accuracy determination of gas concentrations in a binary mixture of H2S and NO2. For this purpose we developed a newly conceived pre-processing method for data feeding of a standard neural network. Pre-processing exploits dimension compaction of the vectorial signal of the sensor array. This operation is being accomplished by an auxiliary auto-associative network, separately trained from the main one. Our results present unprecedented accuracy even when applied to a validation-data set and appear to be of widespread application.
Gnani, Dario; Guidi, Vincenzo; Ferroni, Matteo; G., Faglia; G., Sberveglieri
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1203109
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