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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2017
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| Materias: | |
| 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 |