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STUDY ON PREDICTION METHODS FOR SUBGRADE SETTLEMENT OF HIGH-SPEED RAILWAY USING GRAY NEURAL NETWORK

机译:灰色神经网络研究高速铁路路基沉降预测方法研究

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Prediction methods for subgrade settlement are an important issue in the construction of high-speed railway, grey theory GM(1, 1) and BP neural network model are widely used to predict.These two models have unique advantages compared to common models, despite of limitations.So we combined with the advantages of the grey theory GM(1, 1) model and BP neural network model in this article and put forward a new linear combination forecast model.The data measured in Tianjin-Qinhuangdao Passenger Dedicated Line subgrade settlement is analyzed with this new model in this paper.The results show that both the advantages of the GM(1, 1) model and BP neural network model are absorbed in this new combination model and it has higher prediction accuracy.Thus, its wide application prospects can be expected.
机译:路基解决的预测方法是建设高速铁路,灰色理论通用(1,1)和BP神经网络模型的重要问题广泛用于预测。尽管有两种型号的两种型号具有独特的优势限制。所以我们与本文中的灰色理论GM(1,1)模型和BP神经网络模型的优势结合起来,提出了一种新的线性组合预测模型。在天津 - 秦皇岛乘客专用线路路基结算中测量的数据本文用这种新模型进行了分析。结果表明,GM(1,1)模型和BP神经网络模型的优点在这种新的组合模型中被吸收,并且具有更高的预测精度。因此,其广泛的应用前景可以预料。

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