自适应增强优化的瓦斯涌出量预测模型.

In order to improve the prediction ability of gas emission in mining face, a prediction model based on gate recurrent unit (GRU) is proposed to predict the gas emission by using the relevant influencing factors of gas emission. The initialization process of the sparrow search algorithm is improved, and the improved sparrow algorithm is used to optimize the hyper parameters affecting the GRU prediction model, so as to improve the prediction accuracy of gas emission. Combined with the adaptive enhancement ability of AdaBoost algorithm, an adaptive enhanced optimization gas emission prediction model ( ISSA-GRU-AdaBoost model ) is constructed, and the prediction index features are extracted by using principal component analysis to improve the rapidity of prediction. The model is compared with PSO-ELM model, QPSO-LSTM model, PSO-BP model and SSA-SVM model. The results show that the prediction accuracy of ISSA-GRU-AdaBoost prediction model is higher. [ABSTRACT FROM AUTHOR]