Skip Navigation Links
Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu


A Bispectrum based Algorithm for Inferring Directional Coupling in Uni-Directionally Connected Chaotic Oscillators with Significant Frequency Mismatch

Journal of Applied Nonlinear Dynamics 12(1) (2023) 31--37 | DOI:10.5890/JAND.2023.03.002

Kazimieras Pukenas

Department of Health Promotion and Rehabilitation, Lithuanian Sports University, Sporto 6, LT-44221, Kaunas, Lithuania

Download Full Text PDF

 

Abstract

In this study, we present a new method for assessing the directional coupling between drive and noisy response chaotic oscillators with significant frequency mismatch. This method is based on the phase-amplitude coupling between the drive oscillator and response oscillator. Specifically, the weak phase-amplitude coupling effect is calculated as the magnitude squared coherence between the intrinsic frequency of the drive oscillator and the oscillation frequency of the dynamics of the bispectral energy of the response oscillator. The bispectral energy of the response oscillator at the selected combination of the frequencies is estimated using the Blackman-Harris sliding window with a sharp peak at the central frequency, and the magnitude squared coherence is calculated based on the moving block-bootstrap (MBB) technique. Application of the proposed approach to master-slave R\"{o}ssler systems and coupled Van der Pol oscillators with a frequency ratio of $ {\sim}$1:4 show that the new algorithm is well-suited for assessing the presence of coupling with a priori known direction between master and noisy slave oscillators at signal-to-noise ratios (SNR) up to 12 dB, especially in the case of weak and moderate coupling.

References

  1. [1]  Bollt, E.M., Sun, J., and Runge J. (2018), Introduction to focus issue: Causation inference and information flow in dynamical systems: Theory and applications, Chaos 28, 075201.
  2. [2]  Huang, Y., Fu, Z., and Franzke, C.L.E. (2020), Detecting causality from time series in a machine learning framework, Chaos, 30, 063116.
  3. [3]  Palu\v{s}, M. and Vejmelka, M. (2007), Directionality of coupling from bivariate time series: How to avoid false causalities and missed connections, Physical Review E, 75, 056211.
  4. [4]  Smirnov, D.A. and Andrzejak, R.G. (2005), Detection of weak directional coupling: Phase-dynamics approach versus state-space approach, Physical Review E, 71, 036207.
  5. [5]  Chicharro, D. and Andrzejak, R.G. (2009), Reliable detection of directional couplings using rank statistics, Physical Review E, 80, 026217.
  6. [6]  Sugihara, G., May, R., Ye, H., Hsieh, C.H., Deyle, E., Fogarty, M., and Munch, S. (2012), Detecting causality in complex ecosystems, Science, 338, 496-500.
  7. [7]  Coufal, D., Jakub{i}k, J., Jajcay, N., Hlinka, J., Krakovsk{a}, A., and Palu\v{s}, M. (2017), Detection of coupling delay: a problem not yet solved, Chaos, 27, 083109.
  8. [8]  Krakovsk{a}, A., Jakub{i}k, J., and Chvostekov{a}, M. (2018), Comparison of six methods for the detection of causality in a bivariate time series, Physical Review E, 97, 042207.
  9. [9]  Roya, S. Jantzen, B. (2018), Detecting causality using symmetry transformations, Chaos, 28, 075305.
  10. [10]  Rosenblum, M.G. and Pikovsky, A.S. (2003), Detecting direction of coupling in interacting oscillators, Physical Review E, 64, 045202.
  11. [11]  Smirnov, D.A. and Bezruchko, B.P. (2003), Estimation of interaction strength and direction from short and noisy time series, Physical Review E, 68, 046209.
  12. [12]  Pukenas, K. (2020), An efficient algorithm for inferring functional connectivity between drive and noisy response chaotic oscillators with significant frequency mismatch, Pramana - Journal of Physics, 94(1), 1-5.
  13. [13]  Nikias, C.L., Mendel, J.M. (1993), Signal processing with higher-order spectra, IEEE Signal processing magazine, 10(3), 10-37.
  14. [14]  Stack, J.R., Hartley, R.G., Habetler, T.G. (2004), An amplitude modulation detector for fault diagnosis in rolling element beatings, IEEE Transactions on Industrial Electronics, 51(5), 1097-1102.
  15. [15]  Zoubir, A. and Iskander, R. (2004), Bootstrap Techniques for Signal Processing, New York: Cambridge University Press.
  16. [16]  Mader, M., Mader, W., Sommerlade, L., Timmer, J., and Schelter, B. (2013), Block-bootstrapping for noisy data, Journal of Neuroscience Methods, 219, 285-291.
  17. [17]  Maiz, S., Elbadaoui, M., Bonnardot, F., and Serviere, C. (2014), New second order cyclostationary analysis and application to the detection and characterization of a runner's fatigue, Signal Processing, 102, 188-200.
  18. [18]  Swami, A., Mendel, C.M., and Nikias, C.L. (1996), Higher-Order Spectral Analysis Toolbox for Use with Matlab, Mathworks.
  19. [19]  Laiou, P. and Andrzejak, R.G. (2017), Coupling strength versus coupling impact in nonidentical bidirectionally coupled dynamics, Physical Review E, 95, 012210.