标题:Component Pursuit from Noisy Measurements
作者:Zhao, Yongjian ;Jiang, Bin
通讯作者:Zhao, Yongjian
作者机构:[Zhao, Yongjian ;Jiang, Bin ] Shandong University, Weihai, Shandong; 264209, China
会议名称:2018 International Conference on Smart Grid and Electrical Automation, ICSGEA 2018
会议日期:9 June 2018 through 10 June 2018
来源:Proceedings - 2018 International Conference on Smart Grid and Electrical Automation, ICSGEA 2018
出版年:2018
页码:147-150
DOI:10.1109/ICSGEA.2018.00044
关键词:Component; Noise; Property; Pursuit; Statistics; Variable
摘要:To achieve efficient component pursuit from noisy measurements, a learning algorithm is presented that combines standard gradient principle and the standard stochastic approximations. By extending the linear predictor principle from noise-free case, a proper objective function is introduced which has the same generic form as that for the noise-free case. Extensive computer simulations are performed to illustrate the power of the presented technique. © 2018 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061328062&doi=10.1109%2fICSGEA.2018.00044&partnerID=40&md5=70e39ca58433a3d466271474d486c149
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