NDLI: An improved on-line extreme learning machine algorithm for sunspot number prediction

Content Provider	IEEE Xplore Digital Library
Author	Li Bin Rong Xuewen
Copyright Year	2013
Description	Author affiliation: Sch. of Sci., Shandong Polytech. Univ., Jinan, China (Li Bin) \|\| Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China (Rong Xuewen)
Abstract	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.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	660
Ending Page	664
File Size	328944
Page Count	5
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximations Extreme learning machine Neural networks Time series analysis Chaotic time series prediction On-line learning Prediction algorithms Approximation algorithms Electronic mail Sunspot
Content Type	Text
Resource Type	Article

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