Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements

Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements

The neuro-fuzzy network applying Takagi-Sugeno-Kang (TSK) fuzzy reasoning for the calibration of the semiconductor sensor array is developed in this paper. The structure, as well as the learning algorithm of the neuro-fuzzy network, is presented and tested on the example of estimation of the concent...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement 2004-06, Vol.53 (3), p.630-637
Hauptverfasser: Osowski, S., Linh, T.H., Brudzewski, K.
Format: Artikel
Sprache:eng
Schlagworte:
Calibration
Fuzzy neural networks
Fuzzy reasoning
Fuzzy systems
Gas detectors
Input variables
Nose
Pollution measurement
Sensor arrays
Testing
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Zusammenfassung:The neuro-fuzzy network applying Takagi-Sugeno-Kang (TSK) fuzzy reasoning for the calibration of the semiconductor sensor array is developed in this paper. The structure, as well as the learning algorithm of the neuro-fuzzy network, is presented and tested on the example of estimation of the concentration of gas components in the gaseous mixture (so-called artificial nose problem). The results of numerical experiments are presented and discussed.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2004.827318