Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. I...

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Detalles Bibliográficos
Autores principales: Ma, Zhiyuan, Luo, Guangchun, Qin, Ke, Wang, Nan, Niu, Weina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876707/
https://www.ncbi.nlm.nih.gov/pubmed/29494543
http://dx.doi.org/10.3390/s18030742

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876707/
https://www.ncbi.nlm.nih.gov/pubmed/29494543
http://dx.doi.org/10.3390/s18030742

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