Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm
Abstract 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 em...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44546-0 |