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In view of low prediction accuracy and slow speed of traditional prediction methods for strip crown, a weighted prediction model based on random forest (RF) and support vector machine (SVM) was established. The parameters of models based on RF, SVM, and a combination of RF and SVM were optimized respectively by adopting the improved coati optimization algorithm (ICOA), so as to improve crown prediction accuracy. A 1 580 mm production line of a hot-rolling mill in one company was taken in a simulation research on crown prediction based on its actual measurement. The root mean square error of the weighted prediction model based on RF and SVM is 2.23 μm. It is found that this weighted prediction model has its prediction accuracy increased by 7.08% and 2.62% respectively, compared with the models based on RF and SVM respectively.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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