基于 PCA-Elman 的浓密机泥层高度在线预测研究

摘要

To deal with the problem of thickener mud thickness online prediction,thickener mud height online prediction method based on neural network model of Principal Component Analysis (PCA) and Elman was proposed.This method firstly using SPSS software PCA on multiple sets of original on-site parameters affecting thickener mud height,extract the principal component as input of Elman neural net-work model,finally obtained thickener mud height online prediction results.The results show that aver-age accuracy of thickener mud height by PCA-Elman online prediction reach 91.5%,the prediction method is feasible.%为解决浓密机泥层厚度在线预测问题，提出了基于主成分分析（PCA）与 Elman 神经网络模型的浓密机泥层高度在线预测方法。该方法首先利用 SPSS 软件对影响浓密机泥层高度的多组原始现场参数进行 PCA，提取主成分作为 Elman 神经网络模型的输入，最终得到浓密机泥层高度的在线预测结果。结果表明：PCA-Elman 在线预测浓密机泥层高度的平均准确度达91．5％，该预测方法是可行的。