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Neural Computing and Applications

, Volume 31, Issue 3, pp 637–652 | Cite as

An improved kernel-based incremental extreme learning machine with fixed budget for nonstationary time series prediction

  • Wei Zhang
  • Aiqiang XuEmail author
  • Dianfa Ping
  • Mingzhe Gao
Original Article
  • 198 Downloads

Abstract

In order to curb the model expansion of the kernel learning methods and adapt the nonlinear dynamics in the process of the nonstationary time series online prediction, a new online sequential learning algorithm with sparse update and adaptive regularization scheme is proposed based on kernel-based incremental extreme learning machine (KB-IELM). For online sparsification, a new method is presented to select sparse dictionary based on the instantaneous information measure. This method utilizes a pruning strategy, which can prune the least “significant” centers, and preserves the important ones by online minimizing the redundancy of dictionary. For adaptive regularization scheme, a new objective function is constructed based on basic ELM model. New model has different structural risks in different nonlinear regions. At each training step, new added sample could be assigned optimal regularization factor by optimization procedure. Performance comparisons of the proposed method with other existing online sequential learning methods are presented using artificial and real-word nonstationary time series data. The results indicate that the proposed method can achieve higher prediction accuracy, better generalization performance and stability.

Keywords

Time series prediction Extreme learning machine Online modeling Fixed budget Sparsity measures Adaptive regularization 

Notes

Acknowledgements

The research was supported by National Science Foundation of China under Grant Nos. 61571454.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Wei Zhang
    • 1
  • Aiqiang Xu
    • 1
    Email author
  • Dianfa Ping
    • 2
  • Mingzhe Gao
    • 1
  1. 1.Office of Research and DevelopmentNaval Aeronautical and Astronautical UniversityYantaiPeople’s Republic of China
  2. 2.Department of Electronic and Information EngineeringNaval Aeronautical and Astronautical UniversityYantaiPeople’s Republic of China

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