Titlebook: Estimating Ore Grade Using Evolutionary Machine Learning Models; Mohammad Ehteram,Zohreh Sheikh Khozani,Maliheh Abb Book 2023 The Editor(s

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書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models影響因子(影響力)




書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models影響因子(影響力)學(xué)科排名




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書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models被引頻次




書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models被引頻次學(xué)科排名




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書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models年度引用學(xué)科排名




書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models讀者反饋




書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models讀者反饋學(xué)科排名

Innocence

antidepressant

Predicting Aluminum Oxide Grade,, MLP-NMR, MLP-SSA, MLP-PSO, MLP-GA, and MLP models were 10.12, 10.98, 11.12, 12.23, 14.45, and 15.56 at the testing level. The Nash Sutcliffe efficiencies (NSE) of the BMA, MLP-NMR, MLP-SSA, MLP-PSO, MLP-GA, and MLP models were 0.94, 0.92, 0.89, 0.86, 0.84, and 0.82 at the testing level.

Fibrillation

低位的人或事

Interdict

Interdict

Intellectual

搖曳的微光

Estimating Iron Ore Grade,FA, GMDH-PSO, GMDH-GA, and GMDH were 4.55, 5.12, 5.54, 5.89, and 5.91. At the testing level, the GMDH-SSA decreased the MAE of the GMDH-SCA, GMDH-FFA, GMDH-PSO, GMDH-GA, and GMDH by 4.7, 14, 16, and 17%, respectively. The optimized GMDH models had a high potential for estimating iron ore grade.

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