The Analysis of Building Subsidence Prediction Based on Grey Model Combined with Radial Basis Neural Network

机译：基于径向基神经网络的灰色模型建筑沉降预测分析

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In this paper, a new prediction model named RBNN-GM(1,1) (Radial Basis Neural Network-Grey Model) model was constructed and used for the analysis of building subsidence prediction for the Palms Together Dagoba in Famen Temple in Shaanxi Province in China. The constructed model can make full use of the advantages of few samples and little information predicting in Grey Theory and swift and self-learning in RBNN. The prediction results show that the combined model is more effective than the common grey model. The proposed combined model for building subsidence prediction may offer scientific rationale for estimating whether the building transmutation exceeds the criterion and provide reference for taking the corresponding safety measures.

机译：本文建立了一种名为RBNN-GM（1,1）（径向基神经网络 - 灰色模型）模型的新预测模型，并用于分析陕西省名寺的棕榈泥的建筑沉降预测中国。构造的模型可以充分利用少数样本和灰色理论和迅速的信息预测的优点，并在RBNN中自学。预测结果表明，组合模型比公共灰色模型更有效。建议的建筑物沉降预测的组合模型可以提供科学理由，用于估计建筑嬗变是否超过标准，并为采取相应的安全措施提供参考。

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《International Conference on Technology of Architecture and Structure》|2012年||共5页
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Yan Bai; Qingchang Ren; Fankui Zeng;
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中图分类 TB3-532;
关键词
Prediction Model; Subsidence Predicting; Neural Network; Grey Model.;

机译：预测模型;沉降预测;神经网络;灰色模型。;

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The Analysis of Building Subsidence Prediction Based on Grey Model Combined with Radial Basis Neural Network

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