Prediction of aluminum yield from BiLSTM aluminum reduction pots based on random forest feature selection
The aluminum yield from aluminum reduction pots requires the judgment of pot conditions based on expert experience,and the experience level determines the accuracy of the aluminum yield decision.With respect to the prediction of aluminum yield from aluminum reduction pots,this paper propo-ses a prediction model for aluminum yield from RF-BiLSTM aluminum reduction pots based on random forest(RF)feature selection.Based on the BiL-STM model,the random forest algorithm is used to reduce the dimensionality of features which are entered the BiLSTM model,and the optimized features are compared with different models.The experimental results show that compared with the LSTM method,the mean absolute error(MAE)of RF-BiL-STM is reduced by 21.01,which is better than the existing method,thus providing a certain reference value for the prediction of aluminum yield from alu-minum reduction pots.
NETL
NSTL
万方数据
维普
