| 基于小波去噪改进神经网络拱顶下沉预测研究 |
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| 引用本文： | 周晓菲,王泽根.基于小波去噪改进神经网络拱顶下沉预测研究[J].四川测绘,2011(6):249-251. |
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| 作者姓名： | 周晓菲 王泽根 |
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| 作者单位： | 61683部队;西南石油大学土木工程与建筑工程学院; |
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| 摘 要： | 隧道拱顶下沉监测数据中含有大量的随机误差，为了消除或者消弱随机误差的干扰，本文对实测数据进行小波去噪，使数据更真实性。针对传统BP神经网络预测精度差、收敛慢的问题，通过改进的BP神经网络对去噪的数据进行预测。实验结果表明，并与传统BP神经网络相对比，小波去噪的改进神经网络收敛速度加快，精度提高，预测效果显著提高，适用于拱顶下沉的预测研究。
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| 关 键 词： | 拱项下沉 神经网络 小波去噪 收敛速度 预测研究 |
Prediction Research of Vault Sink Based on an Improved Neural Network of Wavelet De-noising |
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| ZHOU Xiao-fei WANG Ze-gen.Prediction Research of Vault Sink Based on an Improved Neural Network of Wavelet De-noising[J].Surveying and Mapping of Sichuan,2011(6):249-251. |
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| Authors: | ZHOU Xiao-fei WANG Ze-gen |
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| Institution: | ZHOU Xiao-fei1 WANG Ze-gen2(1.61683 Army of PLA,Beijing 100094,China,2.School of Civil & Architecture,Southwest Petroleum University,Chengdu 610500,China) |
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| Abstract: | Vault sink of tunnel contains a lot of random error.In order to eliminate or weaken interference of random error,the measured data was processed by wavelet de-noising that made the data more authenticity in the paper.Aiming at problems such as poor precision and slow convergence about BP neural network prediction,de-noising data was predicted by the improved BP neural network,which compared with traditional BP neural network.Experimental results showed the improved neural network of wavelet de-noising made ... |
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| Keywords: | Vault sink Neural network Wavelet de-noising Convergence rate Prediction research |
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