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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
Full text not available from this repository.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 |
|---|---|
| Subjects: | Q Science > QD Chemistry |
| Journal or Publication Title: | SENSORS AND ACTUATORS B-CHEMICAL |
| Publisher: | ELSEVIER SCIENCE SA LAUSANNE |
| ISSN: | 0925-4005 |
| Date: | 31 January 1996 |
| Volume: | 30 |
| Number: | 3 |
| Number of Pages: | 6 |
| Page Range: | pp. 185-190 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/18907 |
Data sourced from Thomson Reuters' Web of Knowledge
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