Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification

This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-proce...

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Detalles Bibliográficos
Autores principales: Zhao, Wei, Meng, Qing-Hao, Zeng, Ming, Qi, Pei-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751720/
https://www.ncbi.nlm.nih.gov/pubmed/29292772
http://dx.doi.org/10.3390/s17122855

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751720/
https://www.ncbi.nlm.nih.gov/pubmed/29292772
http://dx.doi.org/10.3390/s17122855

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