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Fuzzy neural computing of coffee and tainted-water data from an electronic nose
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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 0925-4005.
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Abstract
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 | ||||
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Subjects: | Q Science > QD Chemistry | ||||
Journal or Publication Title: | SENSORS AND ACTUATORS B-CHEMICAL | ||||
Publisher: | ELSEVIER SCIENCE SA LAUSANNE | ||||
ISSN: | 0925-4005 | ||||
Official Date: | 31 January 1996 | ||||
Dates: |
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Volume: | 30 | ||||
Number: | 3 | ||||
Number of Pages: | 6 | ||||
Page Range: | pp. 185-190 | ||||
Publication Status: | Published |
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