基于神经网络的综放工作面生产能力预测模型

运用人工神经网络的理论方法分析了影响综放工作面生产能力的主要因素，并创建了综放面生产能力神经网络预测模型。采用所建立的预测模型对多个综放工作面进行了预测，预测结果看与现场实际情况相吻合，预测的误差最大值为9．1％，证明所建立的模型对预测综放工作面生产能力具有可靠性和适应性。

Prediction Model of Production Capability of Fully Mechanized Sublevel Caving Face Based on Artificial Neural Network

In this paper, the major influence factors on the production capability of fully mechanized sublevel caving face are analyzed theoretically, and the artificial neural network model is established for predicting the production capability of fully mechanized sublevel caving face. The established prediction model is used to predict the production capabilities of several fully mechanized sublevel caving faces. The predicted results almost are conformable with the actual production capabilities of the caving faces, and the maximum error value of the prediction is 9.1%, proves that this model is of good reliability and adaptability in predicting the production capability of fully mechanized sublevel caving face