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] Shandong Polytech Univ, Sch Sci, Jinan 250353, Peoples R China.; [Li Bin; Rong Xuewen] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, 更多
会议名称：25th Chinese Control and Decision Conference (CCDC)
会议日期：MAY 25-27, 2013
来源：2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)
关键词：On-line learning; Extreme learning machine; Sunspot; Chaotic time series; prediction
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.