A classifier of satellite signals based on the back-propagation neural network

Wei Zhang, Zhong Li, Weidong Xu, Haiquan Zhou
{"title":"A classifier of satellite signals based on the back-propagation neural network","authors":"Wei Zhang, Zhong Li, Weidong Xu, Haiquan Zhou","doi":"10.1109/CISP.2015.7408093","DOIUrl":null,"url":null,"abstract":"In order to achieve the fast classification for Ultra-low-frequency (ULF) electron field data in the Space, this paper designs an electric field classifier based on the back-propagation (BP) neural network with extracting the ULF section electric field waveform data of the Wenchuan earthquake, using the statistical methods to obtain four characteristics of the mean value, mean square error, skewness and kurtosis of an electric field components. Its findings are summarized as follows: (1) This classifier of electric signals is effective with normal data and abnormal data accounting for 72.3% and 27.7% respectively; (2) A momentum factor can improve effectively the BP network performance, which the momentum factor is smaller, the network convergence speed is faster; (3) An adaptive learning factor can reduce effectively the target error. This method is also suitable for the data classification of magnetic field and ion concentration to obtain the seismic precursor knowledge, which has the practical significance for earthquake monitoring and prediction.","PeriodicalId":167631,"journal":{"name":"2015 8th International Congress on Image and Signal Processing (CISP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Congress on Image and Signal Processing (CISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2015.7408093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In order to achieve the fast classification for Ultra-low-frequency (ULF) electron field data in the Space, this paper designs an electric field classifier based on the back-propagation (BP) neural network with extracting the ULF section electric field waveform data of the Wenchuan earthquake, using the statistical methods to obtain four characteristics of the mean value, mean square error, skewness and kurtosis of an electric field components. Its findings are summarized as follows: (1) This classifier of electric signals is effective with normal data and abnormal data accounting for 72.3% and 27.7% respectively; (2) A momentum factor can improve effectively the BP network performance, which the momentum factor is smaller, the network convergence speed is faster; (3) An adaptive learning factor can reduce effectively the target error. This method is also suitable for the data classification of magnetic field and ion concentration to obtain the seismic precursor knowledge, which has the practical significance for earthquake monitoring and prediction.
基于反向传播神经网络的卫星信号分类器
为了实现空间中超低频(ULF)电子场数据的快速分类,本文设计了一种基于BP神经网络的电场分类器,提取汶川地震超低频(ULF)剖面电场波形数据,利用统计方法得到电场分量的均值、均方误差、偏度和峰度四个特征。结果表明:(1)该电信号分类器对正常数据和异常数据的识别率分别为72.3%和27.7%,是有效的;(2)动量因子能有效改善BP网络性能,其中动量因子越小,网络收敛速度越快;(3)自适应学习因子可以有效地减小目标误差。该方法也适用于磁场和离子浓度的数据分类,获取地震前兆知识,对地震监测和预报具有实际意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:604180095
Book学术官方微信