模糊神经控制器

Y. Hayashi, E. Czogala, J. J. Buckley
{"title":"模糊神经控制器","authors":"Y. Hayashi, E. Czogala, J. J. Buckley","doi":"10.1109/FUZZY.1992.258617","DOIUrl":null,"url":null,"abstract":"The authors consider a fuzzy controller that processes fuzzy information. They discuss the model of the fuzzy controller, with fuzzy inputs for error and change in error, using a max-min neural network. A new learning algorithm, a modified delta rule, is derived. The generalization property of the neural net can be used to find a controller output for new fuzzy values of error and change in error. An example is presented showing the applicability of the fuzzy neural controller.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"2 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"52","resultStr":"{\"title\":\"Fuzzy neural controller\",\"authors\":\"Y. Hayashi, E. Czogala, J. J. Buckley\",\"doi\":\"10.1109/FUZZY.1992.258617\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors consider a fuzzy controller that processes fuzzy information. They discuss the model of the fuzzy controller, with fuzzy inputs for error and change in error, using a max-min neural network. A new learning algorithm, a modified delta rule, is derived. The generalization property of the neural net can be used to find a controller output for new fuzzy values of error and change in error. An example is presented showing the applicability of the fuzzy neural controller.<<ETX>>\",\"PeriodicalId\":222263,\"journal\":{\"name\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"volume\":\"2 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"52\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1992.258617\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1992.258617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 52

摘要

作者考虑了一种处理模糊信息的模糊控制器。他们讨论了模糊控制器的模型,模糊输入的误差和误差的变化,使用最大-最小神经网络。提出了一种新的学习算法——改进的delta规则。利用神经网络的泛化特性,可以找到新的误差模糊值和误差变化的控制器输出。最后通过实例说明了模糊神经控制器的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy neural controller
The authors consider a fuzzy controller that processes fuzzy information. They discuss the model of the fuzzy controller, with fuzzy inputs for error and change in error, using a max-min neural network. A new learning algorithm, a modified delta rule, is derived. The generalization property of the neural net can be used to find a controller output for new fuzzy values of error and change in error. An example is presented showing the applicability of the fuzzy neural controller.<>
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:604180095
Book学术官方微信