参与式演化模糊模型

E. Lima, F. Gomide, R. Ballini
{"title":"参与式演化模糊模型","authors":"E. Lima, F. Gomide, R. Ballini","doi":"10.1109/ISEFS.2006.251135","DOIUrl":null,"url":null,"abstract":"This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving participatory learning is used to forecast average weekly inflows for hydroelectric generation purposes and compared with eTS, an evolving modeling technique that uses the notion of potential to dynamically cluster data","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"Participatory Evolving Fuzzy Modeling\",\"authors\":\"E. Lima, F. Gomide, R. Ballini\",\"doi\":\"10.1109/ISEFS.2006.251135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving participatory learning is used to forecast average weekly inflows for hydroelectric generation purposes and compared with eTS, an evolving modeling technique that uses the notion of potential to dynamically cluster data\",\"PeriodicalId\":269492,\"journal\":{\"name\":\"2006 International Symposium on Evolving Fuzzy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 International Symposium on Evolving Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISEFS.2006.251135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Symposium on Evolving Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEFS.2006.251135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38

摘要

参与式学习是一种基于已知或相信的知识来学习和修正信念的方法。参与式学习自然地诱导出无监督动态模糊聚类算法,为构建演化功能模糊模型和自适应模糊系统提供了有效的替代方案。不断发展的参与式学习用于预测水力发电目的的平均每周流入,并与eTS进行比较,eTS是一种不断发展的建模技术,使用潜力的概念来动态聚类数据
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Participatory Evolving Fuzzy Modeling
This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving participatory learning is used to forecast average weekly inflows for hydroelectric generation purposes and compared with eTS, an evolving modeling technique that uses the notion of potential to dynamically cluster data
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