基于拉格朗日优化神经网络的盲多用户检测

Wang Hong-bin, Zhang Li-yi, Wang Hua-kui, Li Fu-ping
{"title":"基于拉格朗日优化神经网络的盲多用户检测","authors":"Wang Hong-bin, Zhang Li-yi, Wang Hua-kui, Li Fu-ping","doi":"10.1049/CP:20070105","DOIUrl":null,"url":null,"abstract":"A kind of Lagrange principle of optimizing neural network is sketched in the paper, it has overcome the traditional defect based on that the neural network which punish function thought exists deal with inequality restraint directly reduce network size and complexity a kind of new optimization neural network Based on the Lagrange neural network, proposed a kind of blind multi-user detection algorithm, and indicated through the computer simulation, this algorithm has the improvement in the error rate performance aspect, the convergence rate also obviously enhances.","PeriodicalId":16222,"journal":{"name":"兰州理工大学学报","volume":"198 1","pages":"150-153"},"PeriodicalIF":0.0000,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Blind multi-user detection based on lagrange optimization neural network\",\"authors\":\"Wang Hong-bin, Zhang Li-yi, Wang Hua-kui, Li Fu-ping\",\"doi\":\"10.1049/CP:20070105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A kind of Lagrange principle of optimizing neural network is sketched in the paper, it has overcome the traditional defect based on that the neural network which punish function thought exists deal with inequality restraint directly reduce network size and complexity a kind of new optimization neural network Based on the Lagrange neural network, proposed a kind of blind multi-user detection algorithm, and indicated through the computer simulation, this algorithm has the improvement in the error rate performance aspect, the convergence rate also obviously enhances.\",\"PeriodicalId\":16222,\"journal\":{\"name\":\"兰州理工大学学报\",\"volume\":\"198 1\",\"pages\":\"150-153\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"兰州理工大学学报\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.1049/CP:20070105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"兰州理工大学学报","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1049/CP:20070105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文概述了一种拉格朗日优化神经网络原理,它克服了传统神经网络存在惩罚函数思想的缺陷,直接处理不等式约束,减小网络规模和复杂度,提出了一种基于拉格朗日神经网络的新型优化神经网络,并通过计算机仿真表明,该算法在错误率性能方面有了改进,收敛速度也明显提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind multi-user detection based on lagrange optimization neural network
A kind of Lagrange principle of optimizing neural network is sketched in the paper, it has overcome the traditional defect based on that the neural network which punish function thought exists deal with inequality restraint directly reduce network size and complexity a kind of new optimization neural network Based on the Lagrange neural network, proposed a kind of blind multi-user detection algorithm, and indicated through the computer simulation, this algorithm has the improvement in the error rate performance aspect, the convergence rate also obviously enhances.
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
5936
×
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