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Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm
Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed in...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579309/ https://www.ncbi.nlm.nih.gov/pubmed/37845268 http://dx.doi.org/10.1038/s41598-023-44546-0 |