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Event-Triggered Distributed Cooperative Learning Algorithms over Networks via Wavelet Approximation

  • Hao Dai
  • Jin Xie
  • Weisheng ChenEmail author
Article
  • 34 Downloads

Abstract

This paper investigates the problem of event-triggered distributed cooperative learning (DCL) over networks based on wavelet approximation theory, where each node only has access to local data which are produced by the same and unknown pattern (map or function). All nodes cooperatively learn this unknown pattern by exchanging learned information with their neighboring nodes under event-triggered strategy in order to remove unnecessary communications, so as to avoid the waste of network resources. For the above problem, two novel event-triggered continuous-time and discrete-time DCL algorithms are proposed to approximate the unknown pattern by using wavelet basis function. The proposed event-triggered DCL algorithms are used to train the optimal weight coefficient matrix of wavelet series. Moreover, the convergence of the proposed algorithms are presented by using the Lyapunov method, and the Zeno behavior is excluded as well by the strictly positive sampling interval. The illustrative examples are presented to show the efficiency and convergence of the proposed algorithms.

Keywords

Event-triggered strategy Distributed cooperative learning (DCL) Wavelet approximation Zeno behavior 

Notes

Acknowledgements

The authors thank the reviewers and the editor for their valuable comments on this paper. This work was supported by the National Natural Science Foundation of China (Grant Numbers: 61503292, 61673308 and 61673014),the Natural Science Foundation of Shaanxi Province (Grant Numbers:2018JM6079) and the Fundamental Research Funds for the Central Universities(Grant No: JB181305).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anPeople’s Republic of China
  2. 2.School of Mathematics and StatisticsXidian UniversityXi’anPeople’s Republic of China

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