An Intelligent Power Factor Correction Approach Based on Linear Regression and Ridge Regression Methods

R. Bayindir, Murat Gök, E. Kabalci, O. Kaplan
{"title":"An Intelligent Power Factor Correction Approach Based on Linear Regression and Ridge Regression Methods","authors":"R. Bayindir, Murat Gök, E. Kabalci, O. Kaplan","doi":"10.1109/ICMLA.2011.34","DOIUrl":null,"url":null,"abstract":"This study introduces an intelligent power factor correction approach based on Linear Regression (LR) and Ridge Regression (RR) methods. The 10-fold Cross Validation (CV) test protocol has been used to evaluate the performance. The best test performance has been obtained from the LR in comparison with RR. The empirical results have evaluated that the selected intelligent compensators developed in this work might overcome the problems met in the literature providing accurate, simple and low-cost solution for compensation.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 10th International Conference on Machine Learning and Applications and Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2011.34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

This study introduces an intelligent power factor correction approach based on Linear Regression (LR) and Ridge Regression (RR) methods. The 10-fold Cross Validation (CV) test protocol has been used to evaluate the performance. The best test performance has been obtained from the LR in comparison with RR. The empirical results have evaluated that the selected intelligent compensators developed in this work might overcome the problems met in the literature providing accurate, simple and low-cost solution for compensation.
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
基于线性回归和岭回归的智能功率因数校正方法
提出了一种基于线性回归(LR)和岭回归(RR)方法的智能功率因数校正方法。使用10倍交叉验证(CV)测试协议来评估性能。与RR相比,LR获得了最佳的测试性能。实证结果表明,本文开发的智能补偿器可以克服文献中遇到的问题,提供准确、简单和低成本的补偿方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Data-Mining Approach to Travel Price Forecasting L1 vs. L2 Regularization in Text Classification when Learning from Labeled Features Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration Speech Rating System through Space Mapping Kernel Methods for Minimum Entropy Encoding
×
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