改进BP神经网络算法的研究与应用

R. Xie, Xinmin Wang, Yan Li, Kairui Zhao
{"title":"改进BP神经网络算法的研究与应用","authors":"R. Xie, Xinmin Wang, Yan Li, Kairui Zhao","doi":"10.1109/ICIEA.2010.5514820","DOIUrl":null,"url":null,"abstract":"As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can be calculated and compared. Finally, choosing a certain type of airplane as the controlled object, the improved BP neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved BP neural network algorithm can realize quicker convergence rate and better tracking accuracy.","PeriodicalId":234296,"journal":{"name":"2010 5th IEEE Conference on Industrial Electronics and Applications","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":"{\"title\":\"Research and application on improved BP neural network algorithm\",\"authors\":\"R. Xie, Xinmin Wang, Yan Li, Kairui Zhao\",\"doi\":\"10.1109/ICIEA.2010.5514820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can be calculated and compared. Finally, choosing a certain type of airplane as the controlled object, the improved BP neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved BP neural network algorithm can realize quicker convergence rate and better tracking accuracy.\",\"PeriodicalId\":234296,\"journal\":{\"name\":\"2010 5th IEEE Conference on Industrial Electronics and Applications\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 5th IEEE Conference on Industrial Electronics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2010.5514820\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 5th IEEE Conference on Industrial Electronics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2010.5514820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 36

摘要

针对迭代次数多、调整速度慢的特点,对标准BP神经网络算法进行了改进。改进了配重调整规则的动量项,使配重调整速度更快,配重调整过程更平稳。实例仿真表明,改进后的BP神经网络算法可以进行迭代计算和比较。最后,选择某型飞机作为被控对象,采用改进的BP神经网络算法设计控制律进行控制指令跟踪,仿真结果表明,改进的BP神经网络算法能够实现更快的收敛速度和更好的跟踪精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and application on improved BP neural network algorithm
As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can be calculated and compared. Finally, choosing a certain type of airplane as the controlled object, the improved BP neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved BP neural network algorithm can realize quicker convergence rate and better tracking accuracy.
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