Fuzzy neural computing of coffee and tainted-water data from an electronic nose
UNSPECIFIED (1996) Fuzzy neural computing of coffee and tainted-water data from an electronic nose. SENSORS AND ACTUATORS B-CHEMICAL, 30 (3). pp. 185-190. ISSN 0925-4005Full text not available from this repository.
In this paper we compare the ability of a fuzzy neural network and a common back-propagation network to classify odour samples that were obtained by an electronic nose employing semiconducting oxide conductometric gas sensors. Two different sample sets have been analysed: first, the aroma of three blends of commercial coffee, and secondly, the headspace of six different tainted-water samples. The two experimental data sets provide an excellent opportunity to test the ability of a fuzzy neural network due to the high level of sensor variability often experienced with this type of sensor. Results are presented on the application of three-layer fuzzy neural networks to electronic nose data. They demonstrate a considerable improvement in performance compared to a common back-propagation network.
|Item Type:||Journal Article|
|Subjects:||Q Science > QD Chemistry|
|Journal or Publication Title:||SENSORS AND ACTUATORS B-CHEMICAL|
|Publisher:||ELSEVIER SCIENCE SA LAUSANNE|
|Date:||31 January 1996|
|Number of Pages:||6|
|Page Range:||pp. 185-190|
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