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Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models
Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-cl...
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| Huvudupphovsmän: | , , , , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
MDPI AG
2019-09-01
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| Serie: | Sensors |
| Ämnen: | |
| Länkar: | https://www.mdpi.com/1424-8220/19/18/3844 |
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