| 基于小波神经网络的隧道变形预测模型研究 |
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| 引用本文： | 胡纪元,;文鸿雁,;周吕,;陈冠宇.基于小波神经网络的隧道变形预测模型研究[J].中南公路工程,2014(4):26-29. |
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| 作者姓名： | 胡纪元 ;文鸿雁 ;周吕 ;陈冠宇 |
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| 作者单位： | [1]广西空间信息与测绘重点实验室,广西桂林541004; [2]桂林理工大学测绘地理信息学院,广西桂林541004; [3]桂林理工大学广西矿冶与环境科学实验中心,广西桂林541004 |
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| 基金项目： | 国家自然科学基金项目（41071294）,广西空间信息与测绘重点实验室资助课题（桂科能1207115-06,1103108-02）,研究生教育创新计划项目（YCSZ2012083）;大学生创新创业训练计划项目（201210596003） |
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| 摘 要： | 针对单一模型在隧道变形预测上精度不高的问题,提出了一种基于小波分析理论的神经网络模型,该模型克服了BP神经网络模型存在的收敛速度慢、结构设计盲目、易陷入局部极小点的缺陷,通过将该模型与时间序列模型、Levenberg-Marquardt法BP神经网络模型、遗传神经网络模型预测的结果比较,可以看出小波神经网络在隧道的变形预测中网络结构更简单、收敛速度更快、预测精度更高。
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| 关 键 词： | 隧道 预测精度 小波神经网络 时间序列模型 遗传神经网络 |
Research on Prediction Model of Tunnel Deformation Based on Wavelet Neural Network |
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| Institution: | HU Jiyuan, WEN Hongyan, ZHOU LV, CHEN Guanyu( 1. Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin, Guangxi 541004, China ; 2. College of Surveying, Mapping and Geoinformation, Guilin University of Teehnology, Guilin Guangxi 541004, China; 3. Guangxi Scientific Experiment Center of Mining, Metallurgy and Environment, Gui- lin, Guangxi 541004, China) |
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| Abstract: | Based on the fact that single model＇s prediction precision in tunnel deformation monitoring is not very high, this paper proposes a Neural Network model based on Wavelet analysis theory, which can overcome slow convergence speed, blindness of structure design and local minimum of BP Neural network. Compared with time series model, Levenberg-Marquardt BP Neural Network model, and GA-BP Neural Network model, prediction results show thatWavelet Neural Network model has a simple network structure, faster convergence speed and higher prediction precision in tunnel deformation monitoring. |
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| Keywords: | tunnel precision wavelet neural network time series model GA-BP neural network |
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