首页> 外文会议>Beijing Jiaotong University;International conference on railway engineering;Beijing Key Laboratory of Track Engineering;China Railway Society >STUDY ON PREDICTION METHODS FOR SUBGRADE SETTLEMENT OF HIGH-SPEED RAILWAY USING GRAY NEURAL NETWORK
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STUDY ON PREDICTION METHODS FOR SUBGRADE SETTLEMENT OF HIGH-SPEED RAILWAY USING GRAY NEURAL NETWORK

机译:基于灰色神经网络的高速铁路路基沉降预测方法研究

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摘要

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:路基沉降的预测方法是高速铁路建设中的一个重要问题,灰色理论GM(1,1)和BP神经网络模型被广泛用于预测。尽管这两个模型与普通模型相比具有独特的优势。因此,本文结合灰色理论GM(1,1)模型和BP神经网络模型的优点,提出了一种新的线性组合预测模型。结果表明,GM(1,1)模型和BP神经网络模型的优点都被该新组合模型吸收,并且具有较高的预测精度。因此,其广阔的应用前景可以预料的。

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