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。
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
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
期刊最新文献
基于计算机模拟和SPR传感策略的大球盖菇多功能鲜味肽筛选及其活性分子作用机制解析 基于区块链的电子健康记录访问控制及安全共享研究 天然大分子基卤胺抗菌材料在创伤感染中的应用研究 MXenes层结构调控及高倍率碱金属离子存储性能研究 移动式酸洗废液处理装置的研究
×
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