| 改进BP神经网络模型在索塔变形预测中的应用 |
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| 引用本文： | 陈帅,黄腾,高大龙.改进BP神经网络模型在索塔变形预测中的应用[J].地理空间信息,2022,20(2):27-32. |
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| 作者姓名： | 陈帅 黄腾 高大龙 |
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| 作者单位： | 河海大学地球科学与工程学院,江苏 南京 2100982,江苏省地质矿产局第一地质大队,江苏 南京 210041 |
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| 摘 要： | 通过分析温度、风速气压、日照和拉索拉力等因素,建立函数关系模型,结合南京长江五桥钢壳-混凝土索塔变形监测实例,采用基于小波阈值去噪、岭回归、BP神经网络的多算法组合模型,进行不同组合算法的实验,预测索塔监测点的坐标,与全站仪观测的索塔变形数据进行比较,得到小波-岭回归-BP神经网.
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| 关 键 词： | 钢-混索塔 小波去噪 岭回归 BP神经网络 络模型预测值最为接近真值 |
Application of Improved BP Neural Network Model in Cable Tower Deformation Prediction |
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| CHEN Shuai,HUANG Teng,GAO Dalong.Application of Improved BP Neural Network Model in Cable Tower Deformation Prediction[J].Geospatial Information,2022,20(2):27-32. |
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| Authors: | CHEN Shuai HUANG Teng GAO Dalong |
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| Institution: | (School of Earth Sciences and Engineering,Hohai University,Nanjing 210098,China;The 1st Geological Brigade of Jiangsu Geology&Mineral Exploration Bureau,Nanjing Jiangsu 210041,China) |
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| Abstract: | Based on the analysis of factors such as temperature,wind pressure,sunshine,and cable tension,we established a functional relation-ship model.And then,combining with the deformation monitoring examples of steel shell-concrete cable tower of Nanjing Yangtze River Five Bridge,we used wavelet threshold denoising,ridge regression and BP neural network multi-algorithm combination model to carry out experi-ments with different combination algorithms,predicted the coordinates of the tower monitoring point,and compared with the tower deformation data observed by the total station to obtain the wavelet-ridge regression-BP neural network model prediction value closest to the true value. |
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| Keywords: | steel-concrete cable tower wavelet denoising ridge regression BP neural network |
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