基于改进小波去噪神经网络的拱顶汇预测研究

Zegen Wang, Fapeng Li, Yan-mei Yang
{"title":"基于改进小波去噪神经网络的拱顶汇预测研究","authors":"Zegen Wang, Fapeng Li, Yan-mei Yang","doi":"10.5503/J.PNSGE.2011.02.004","DOIUrl":null,"url":null,"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 convergence rate accelerate,accuracy improve,prediction result significantly enhance,it was true to prediction research of vault sink.","PeriodicalId":441518,"journal":{"name":"Surveying and mapping","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction Research of Vault Sink Based on an Improved Neural Network of Wavelet De-noising\",\"authors\":\"Zegen Wang, Fapeng Li, Yan-mei Yang\",\"doi\":\"10.5503/J.PNSGE.2011.02.004\",\"DOIUrl\":null,\"url\":null,\"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 convergence rate accelerate,accuracy improve,prediction result significantly enhance,it was true to prediction research of vault sink.\",\"PeriodicalId\":441518,\"journal\":{\"name\":\"Surveying and mapping\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Surveying and mapping\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5503/J.PNSGE.2011.02.004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Surveying and mapping","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5503/J.PNSGE.2011.02.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

隧道拱顶沉降存在大量随机误差。为了消除或减弱随机误差的干扰,本文对测量数据进行了小波去噪处理,使数据更加真实。针对BP神经网络预测精度差、收敛速度慢等问题,与传统BP神经网络相比,采用改进的BP神经网络对降噪数据进行预测。实验结果表明,改进后的小波去噪神经网络的收敛速度加快,精度提高,预测结果明显增强,适用于拱顶汇的预测研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction Research of Vault Sink Based on an Improved Neural Network of Wavelet De-noising
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 convergence rate accelerate,accuracy improve,prediction result significantly enhance,it was true to prediction research of vault sink.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信