An efficient model for systems with complex responses (neural network architecture for nonlinear filtering)

Volker Tresp, I. Leuthausser, M. Schlang, R. Neuneier, K. Abraham-Fuchs, W. Harer
{"title":"An efficient model for systems with complex responses (neural network architecture for nonlinear filtering)","authors":"Volker Tresp, I. Leuthausser, M. Schlang, R. Neuneier, K. Abraham-Fuchs, W. Harer","doi":"10.1109/NNSP.1992.253663","DOIUrl":null,"url":null,"abstract":"Presents a neural network architecture for a restricted class of nonlinear filtering applications. The filter architecture is particularly suited for biomedical and technical applications that require long and complex system responses. The filter architecture was successfully used in a biomedical application for the removal of the cardiac interference from magnetoencephalographic (MEG) data and performed better than standard linear filters and the time-delay neural network.<<ETX>>","PeriodicalId":438250,"journal":{"name":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","volume":"183 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1992.253663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Presents a neural network architecture for a restricted class of nonlinear filtering applications. The filter architecture is particularly suited for biomedical and technical applications that require long and complex system responses. The filter architecture was successfully used in a biomedical application for the removal of the cardiac interference from magnetoencephalographic (MEG) data and performed better than standard linear filters and the time-delay neural network.<>
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
复杂响应系统的有效模型(非线性滤波的神经网络结构)
提出了一种适用于有限非线性滤波应用的神经网络结构。该滤波器架构特别适用于需要长时间和复杂系统响应的生物医学和技术应用。该滤波器结构已成功用于生物医学应用,用于从脑磁图(MEG)数据中去除心脏干扰,并且比标准线性滤波器和延时神经网络表现更好。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Discrete neural networks and fingerprint identification A fast simulator for neural networks on DSPs or FPGAs Hierarchical perceptron (HiPer) networks for signal/image classifications Adaptive decision-feedback equalizer using forward-only counterpropagation networks for Rayleigh fading channels An efficient model for systems with complex responses (neural network architecture for nonlinear filtering)
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1