基于强化学习的音圈电机模糊控制

T.S. Liu, W. Chang
{"title":"基于强化学习的音圈电机模糊控制","authors":"T.S. Liu, W. Chang","doi":"10.1109/CIMA.2005.1662346","DOIUrl":null,"url":null,"abstract":"Dealing with voice coil motors, this paper presents reinforcement learning based fuzzy control, which incorporates characteristics of reinforcement learning into fuzzy control. Fuzzy control has excellent characteristics of dealing with model uncertainty and nonlinearity. To complement and improve fuzzy control, reinforcement learning is used to process rough feedback signals. This work constructs fuzzy rules based model based on input-output data of plants and tune fuzzy membership functions by reinforcement learning","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fuzzy control based on reinforcement learning for voice coil motor\",\"authors\":\"T.S. Liu, W. Chang\",\"doi\":\"10.1109/CIMA.2005.1662346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dealing with voice coil motors, this paper presents reinforcement learning based fuzzy control, which incorporates characteristics of reinforcement learning into fuzzy control. Fuzzy control has excellent characteristics of dealing with model uncertainty and nonlinearity. To complement and improve fuzzy control, reinforcement learning is used to process rough feedback signals. This work constructs fuzzy rules based model based on input-output data of plants and tune fuzzy membership functions by reinforcement learning\",\"PeriodicalId\":306045,\"journal\":{\"name\":\"2005 ICSC Congress on Computational Intelligence Methods and Applications\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 ICSC Congress on Computational Intelligence Methods and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIMA.2005.1662346\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 ICSC Congress on Computational Intelligence Methods and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIMA.2005.1662346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

针对音圈电机,提出了一种基于强化学习的模糊控制方法,将强化学习的特点融入到模糊控制中。模糊控制具有处理模型不确定性和非线性的优良特性。为了补充和改进模糊控制,采用强化学习对粗糙反馈信号进行处理。本文以植物的输入输出数据为基础,构建了基于模糊规则的模型,并通过强化学习对模糊隶属函数进行了调整
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
Fuzzy control based on reinforcement learning for voice coil motor
Dealing with voice coil motors, this paper presents reinforcement learning based fuzzy control, which incorporates characteristics of reinforcement learning into fuzzy control. Fuzzy control has excellent characteristics of dealing with model uncertainty and nonlinearity. To complement and improve fuzzy control, reinforcement learning is used to process rough feedback signals. This work constructs fuzzy rules based model based on input-output data of plants and tune fuzzy membership functions by reinforcement learning
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
A comparison of fuzzy, state space with direct eigenstructure assignment, and PID controller on linearized MIMO plant model Measurement of the cross-sectional contour of H-shaped steel using multiple stereo pairs Feature selection based on bootstrapping Eigenvector methods for automated detection of time-varying biomedical signals Animal toxins: what features differentiate pore blockers from gate modifiers?
×
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