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On the Identification and Generation of Discrete-Time Chaotic Systems with Recurrent Neural Networks

  • Seungwon Lee
  • Sung Hwan Won
  • Iickho Song
  • Seokho Yoon
  • Sun Yong KimEmail author
Original Article
  • 2 Downloads

Abstract

We address the identification and generation of the discrete-time chaotic system (DTCS) with a two-layered recurrent neural network (RNN). First, we propose an identification procedure of the DTCS in which the RNN is required to have less layers than in the conventional procedures. Next, based on Li–Yorke theorem, we propose a generation procedure which enables us to predict a range of chaotic behavior of the DTCS in advance. Simulation results demonstrate that the proposed identification procedure, employing the Levenberg–Marquardt algorithm and a two-layered RNN, requires lower computational complexity than the conventional identification procedures at comparable performance.

Keywords

Discrete-time chaotic system Recurrent neural network (RNN) System generation System identification 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea under Grant NRF-2018R1A2A1A05023192, for which the authors wish to express their appreciation. The authors would also like to thank the Associate Editor and two anonymous reviewers for their constructive suggestions and helpful comments.

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Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Seungwon Lee
    • 1
  • Sung Hwan Won
    • 2
  • Iickho Song
    • 1
  • Seokho Yoon
    • 3
  • Sun Yong Kim
    • 4
    Email author
  1. 1.School of Electrical EngineeringKorea Advanced Institute of Science and TechnologyDaejeonSouth Korea
  2. 2.Nokia Bell LabsSeoulSouth Korea
  3. 3.College of Information and Communication EngineeringSungkyunkwan UniversitySuwonSouth Korea
  4. 4.Department of Electronics EngineeringKonkuk UniversitySeoulSouth Korea

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