Metal Oxide Gas Sensor Drift Compensation Using a Dynamic Classifier Ensemble Based on Fitting

Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 13; H. 7; S. 9160 - 9173
Hauptverfasser: Liu, Hang, Tang, Zhenan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 17.07.2013
Schlagworte:
Algorithms
Artificial Intelligence
Data Interpretation, Statistical
Datasets
dynamic weights
ensemble method
Equipment Design
Equipment Failure Analysis
Fuzzy sets
Gases - analysis
metal oxide sensors
Metal oxides
Metals - analysis
Methods
Neural networks
Olfactometry - instrumentation
Oxides - analysis
Pattern recognition systems
sensor drift
Sensors
Signal processing
Support vector machines
Transducers
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets that are collected at different time periods. In the testing of future datasets, the classifier weights are predicted by fitting functions, which are obtained by the proper fitting of the optimal weights during training. We compare the performance of the DWF method with that of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the DWF method outperforms the other methods considered. Furthermore, the DWF method can be further optimized by applying a fitting function that more closely matches the variation of the optimal weight over time.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s130709160