Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the si...

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Detalles Bibliográficos
Autores principales: Chen, Kun, Liang, Yu, Gao, Zengliang, Liu, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579503/
https://www.ncbi.nlm.nih.gov/pubmed/28786957
http://dx.doi.org/10.3390/s17081830

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579503/
https://www.ncbi.nlm.nih.gov/pubmed/28786957
http://dx.doi.org/10.3390/s17081830

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