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An Improved Normalized Mutual Information Variable Selection Algorithm for Neural Network-Based Soft Sensors
In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960561/ https://www.ncbi.nlm.nih.gov/pubmed/31817459 http://dx.doi.org/10.3390/s19245368 |