Nonlinear Dynamics

, Volume 91, Issue 2, pp 1001–1021 | Cite as

Synchronization control of oscillator networks using symbolic regression

  • Julien Gout
  • Markus Quade
  • Kamran Shafi
  • Robert K. Niven
  • Markus Abel
Original Paper


Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far-reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly effective and interpretable control functions for such systems.


Dynamical systems Synchronization control Genetic programming 


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Julien Gout
    • 1
    • 2
  • Markus Quade
    • 1
    • 2
  • Kamran Shafi
    • 3
  • Robert K. Niven
    • 3
  • Markus Abel
    • 1
    • 2
  1. 1.University of PotsdamPotsdamGermany
  2. 2.Ambrosys GmbHPotsdamGermany
  3. 3.School of Engineering and Information TechnologyUniversity of New South WalesCanberraAustralia

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