时变RBF神经网络及其应用研究

Jing Li, Zhe Wang, Shengzhi Yuan, Haidi Dong
{"title":"时变RBF神经网络及其应用研究","authors":"Jing Li, Zhe Wang, Shengzhi Yuan, Haidi Dong","doi":"10.1109/ICCR55715.2022.10053686","DOIUrl":null,"url":null,"abstract":"The problem of how to approximate unknown time-varying nonlinear functions is researched in this paper. Firstly, a new RBF NN with time-varying weight is proposed to approximate the unknown time-varying nonlinear function. Secondly, the approximate theorem of the proposed time-varying RBF NN is obtained. Accordingly, a conclusion can be drawn that a continuous time-varying nonlinear function defined on finite time interval [0, T] can be approximated by at least a piecewise continuous time-varying weight vector and a finite number of RBF neurons. Finally, simulation examples are given to validate the effectiveness of proposed time-varying RBF NN.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Time-varying RBF NN and Its Application\",\"authors\":\"Jing Li, Zhe Wang, Shengzhi Yuan, Haidi Dong\",\"doi\":\"10.1109/ICCR55715.2022.10053686\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of how to approximate unknown time-varying nonlinear functions is researched in this paper. Firstly, a new RBF NN with time-varying weight is proposed to approximate the unknown time-varying nonlinear function. Secondly, the approximate theorem of the proposed time-varying RBF NN is obtained. Accordingly, a conclusion can be drawn that a continuous time-varying nonlinear function defined on finite time interval [0, T] can be approximated by at least a piecewise continuous time-varying weight vector and a finite number of RBF neurons. Finally, simulation examples are given to validate the effectiveness of proposed time-varying RBF NN.\",\"PeriodicalId\":441511,\"journal\":{\"name\":\"2022 4th International Conference on Control and Robotics (ICCR)\",\"volume\":\"160 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 4th International Conference on Control and Robotics (ICCR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCR55715.2022.10053686\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 4th International Conference on Control and Robotics (ICCR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCR55715.2022.10053686","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文研究了未知时变非线性函数的逼近问题。首先,提出了一种新的时变权值RBF神经网络来逼近未知时变非线性函数;其次,给出了时变RBF神经网络的近似定理。由此可以得出,定义在有限时间区间[0,T]上的连续时变非线性函数可以被至少一个分段连续时变权向量和有限个RBF神经元所近似。最后通过仿真实例验证了所提时变RBF神经网络的有效性。
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
Research on Time-varying RBF NN and Its Application
The problem of how to approximate unknown time-varying nonlinear functions is researched in this paper. Firstly, a new RBF NN with time-varying weight is proposed to approximate the unknown time-varying nonlinear function. Secondly, the approximate theorem of the proposed time-varying RBF NN is obtained. Accordingly, a conclusion can be drawn that a continuous time-varying nonlinear function defined on finite time interval [0, T] can be approximated by at least a piecewise continuous time-varying weight vector and a finite number of RBF neurons. Finally, simulation examples are given to validate the effectiveness of proposed time-varying RBF NN.
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
期刊最新文献
Mobile Humanoid Robot Control through Object Movement Imagery Optimization of Two-end Access Platform Automated Warehouse Storage Allocation Long-Tailed Object Mining Based on CLIP Model for Autonomous Driving Node Deployment and Energy Saving Optimization Method for Wireless Sensor Networks Based on Q-learning Off-policy Q-learning-based Tracking Control for Stochastic Linear Discrete-Time Systems
×
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