基于PCA与Elman网络的地下矿山岩层移动

 由于地下金属矿床地质与开采条件的复杂性,影响岩层移动的因素错综复杂且相互影响,使得对岩层移动的预测具有很大的不确定性。大量的样本数据减慢了神经网络的训练速度,并且使得神经网络不稳定。将主成分分析(PCA)与Elman网络相结合构建模型,对地下矿山岩层移动角进行预测研究。利用主成分分析对原始数据进行预处理,提取原信息的主成分,将输入变量减少且互不相关,提高神经网络训练速度；用Elman网络对训练样本进行训练,进而利用训练好的网络对预测样本进行预测,与不采用PCA时的预测结果相比,采用PCA的预测结果更为准确,通过期望输出与实际输出的对比,相对误差都在5%以内,其预测的结果精度高,表明了PCA与Elman网络相结合对地下矿山岩层移动进行研究是可行的。

Rock Displacement Prediction for Underground Metal Mines Based on PCA and Elman Feedback Neural Network

With the complexity of the underground metal geological and mining conditions, the influencing factors of the rock displacement are an complicated issue and they are interactive, which leads to the great uncertainty of the prediction of the rock displacement. Large amounts of sample data slow down the training process of the neural network, which makes the neural network instable. The PCA (Principal Component Analysis) is, therefore, used, combined with the Elman network, to build the model for prediction of the underground mining rock displacement angle. The principal component analysis is used in the raw data pre-processing to extract the main ingredients from the original information, to reduce the amount of input data and make them unrelated, thus to speed up the neural network training process; the samples are then trained with the Elman network, and with the trained network to make predictions for the samples. Compared with the predicted results obtained without using the PCA, the predicted results obtained by using the PCA are more accurate. Through comparing the expected output with the actual output, the relative errors are less than 5%, which shows that the PCA combined with the Elman network is good for the prediction of the underground mining rock displacement.