标题:An improved on-line extreme learning machine algorithm for sunspot number prediction
作者:Li, Bin ;Rong, Xuewen
作者机构:[Li, Bin ] School of Science, Shandong Polytechnic University, Jinan, 250353, China;[Li, Bin ;Rong, Xuewen ] School of Control Science and Engineering 更多
会议名称:2013 25th Chinese Control and Decision Conference, CCDC 2013
会议日期:25 May 2013 through 27 May 2013
来源:2013 25th Chinese Control and Decision Conference, CCDC 2013
出版年:2013
页码:660-664
DOI:10.1109/CCDC.2013.6561006
关键词:Chaotic time series prediction; Extreme learning machine; On-line learning; Sunspot
摘要:The single hidden layer feed-forward neural networks has simple structure, good approximation performance on real applications. The on-line learning algorithms based on the single hidden layer feed-forward neural networks have the ability of real time on-line learning and are suitable to sequential learning environments and applications. The sunspot number prediction is an important content in the space environment forecast. According to the strong nonlinear characteristics and difficult mid long term prediction problem for sunspot, an improved on-line extreme learning machine with good approximation ability and generation performance is applied to sunspot number chaotic time series prediction in this paper, The improved algorithm updates the output-layer weights with a Givens QR decomposition based on the orthogonalized least squares algorithm. Simulation results show that the improved algorithm can avoid the singular of the hidden layer output matrix and obtain better network performance. The improved algorithm provides a comparing fast and real time on-line learning ability for sunspot chaotic time series space environmental prediction. © 2013 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-s2.0-84882795292&doi=10.1109%2fCCDC.2013.6561006&partnerID=40&md5=f3eed6500eb700ecb78c99074ec44d29
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