ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


For citation:

Sysoev I. V., Kulminskiy D. D., Ponomarenko V. I., Prokhorov M. D. Reconstruction of coupling architecture and parameters of time-delayed oscillators in ensembles from time series. Izvestiya VUZ. Applied Nonlinear Dynamics, 2016, vol. 24, iss. 3, pp. 21-37. DOI: 10.18500/0869-6632-2016-24-3-21-37

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
(downloads: 157)
Language: 
Russian
Article type: 
Article
UDC: 
537.86

Reconstruction of coupling architecture and parameters of time-delayed oscillators in ensembles from time series

Autors: 
Sysoev Ilya Vyacheslavovich, Saratov State University
Kulminskiy Danil Dmitrievich, Saratov Branch of Kotel`nikov Institute of Radiophysics and Electronics of Russian Academy of Sciences
Ponomarenko Vladimir Ivanovich, Saratov Branch of Kotel`nikov Institute of Radiophysics and Electronics of Russian Academy of Sciences
Prokhorov Mihail Dmitrievich, Saratov Branch of Kotel`nikov Institute of Radiophysics and Electronics of Russian Academy of Sciences
Abstract: 

Purpose. To suggest a new approach to reconstruction of couping architecture and individual parameters of first-order time-delayed oscillators from experimental series of their oscillations. Method. The method is based on minimization of target function, which characterizes a distance between points of nonlinear function of a current oscillator, which is to be reconstructed. Then estimated coupling coefficients are split into significant and insignificant. Minimization of target function is processed with least squares routine. Delay time is estimated as a trial delay corresponding to a minimum of target function over all trial delays. Results. Efficiency of the proposed method was demonstrated in numerical experiment from time series of an ensemble of diffusively coupled nonidentical Mackey–Glass oscillators in presence of noise. Also a hardware experiment was considered in which resistively coupled generators with delay line were studied. The method demonstrated higher computational efficiency than previously suggested approaches due to use of not iterative algorithms for target function minimization and significant coefficient selection. Herewith estimates of coupling coefficients and inertance parameter are asymptotically unbiased. Discussion. The proposed approach may be useful for reconstruction of parameters of elements and coupling architecture in systems of different nature: radioengineering, biological or others, which can be described using first-order time-delay equations.

Reference: 
  1. Afraimovich V.S., Nekorkin V.I., Osipov G.V., Shalfeev V.D. Stability, Structures, and Chaos in Nonlinear Synchronization Networks. Singapore: World Scientific, 1995.
  2. Pikovsky A., Rosenblum M., Kurths J. Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge University Press, United Kingdom, 2003.
  3. Boccaletti S., Latora V., Moreno Y., Chavez M., Hwang D.U. // Phys. Rep. 2006. Vol. 424. P. 175.
  4. Bezruchko B., Smirnov D. Extracting Knowledge From Time Series. Springer: Complexity, 2010.
  5. Timme M. Revealing network connectivity from response dynamics // Phys. Rev. Lett. 2007. Vol. 98. 224101.
  6. Smirnov D.A., Bezruchko B.P. Detection of couplings in ensembles of stochastic oscillators // Phys. Rev. E. 2009. Vol. 79. 046204.
  7. Kaminski M., Ding M., Truccolo W.A., Bressler S.L. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance // Biol. Cybern. 2001. Vol. 85. P. 145.
  8. Sysoev I.V., Sysoeva M.V. Detecting changes in coupling with Granger causality method from time series with fast transient processes // Physica D. 2015. Vol. 309. P. 9.
  9. Liu H., Lu J.-A., Lu J., Hill D.J. Structure identification of uncertain general complex dynamical networks with time delay // Automatica. 2009. Vol. 45. P. 1799.
  10. Xu Y., Zhou W., Fang J. Topology identification of the modified complex dynamical network with non-delayed and delayed coupling // Nonlinear Dynamics. 2012. Vol. 68. P. 195.
  11. Yang X.L., Wei T. Revealing network topology and dynamical parameters in delay-coupled complex network subjected to random noise // Nonlinear Dynamics. 2015. Vol. 82. P. 319
  12. Chen J., Lu J., Zhou J. Topology identification of complex networks from noisy time series using ROC curve analysis // Nonlinear Dynamics. 2014. Vol. 75. P. 761.
  13. Zhang Z., Zheng Z., Niu H., Mi Y., Wu S., Hu G. Solving the inverse problem of noise-driven dynamic networks // Phys. Rev. E. 2015. Vol. 91. 012814.
  14. Wens V. Investigating complex networks with inverse models: Analytical aspects of spatial leakage and connectivity estimation // Phys. Rev. E. 2015. Vol. 91. 012823.
  15. Hale J.K., Lunel S.M.V. Introduction to Functional Differential Equations. New York: Springer, 1993.
  16. Kuang Y. Delay Differential Equations with Applications in Population Dynamics. Boston: Academic Press, 1993.
  17. Bocharov G.A., Rihan F.A. Numerical modelling in biosciences using delay differential equations // J. Comp. Appl. Math. 2000. Vol. 125. P. 183.
  18. Mincheva M., Roussel M.R. Graph-theoretic methods for the analysis of chemical and biochemical networks. II. Oscillations in networks with delays // J. Math. Biol. 2007. Vol. 55. P. 87.
  19. Heiligenthal S., Jungling T., D’Huys O., Arroyo-Almanza D.A., Soriano M.C.,  Fischer I., Kanter I., Kinzel W. Strong and weak chaos in networks of semiconductor lasers with time-delayed couplings // Phys. Rev. E. 2013. Vol. 88. 012902.
  20. Wu X., Sun Z., Liang F., Yu C. Online estimation of unknown delays and parameters in uncertain time delayed dynamical complex networks via adaptive observer // Nonlinear Dynamics. 2013. Vol. 73. P. 1753.
  21. Sysoev I.V., Prokhorov M.D., Ponomarenko V.I., Bezruchko B.P. // Tech. Phys. 2014. Vol. 59. P. 1434.
  22. Ponomarenko V. I., Prokhorov M. D., Karavaev A. S., Bezruchko B. P. Recovery of parameters of delayed-feedback systems from chaotic time series // Journal of Experimental and Theoretical Physics. 2005. Vol. 100. Issue 3. P. 457.
  23. Prokhorov M.D., Ponomarenko V.I. Estimation of coupling between time-delay systems from time series // Phys. Rev. E. 2005. Vol. 72. 016210.
  24. Prokhorov M.D., Ponomarenko V.I. Reconstruction of time-delay systems using small impulsive disturbances // Phys. Rev. E. 2009. Vol. 80. 066206.
  25. Mandel I.D. Cluster Analysis. Moscow: Finance and Statistics, 1988. 176 p. (in Russian).
  26. Mackey M.C., Glass L. Oscillations and chaos in physiological control systems //Science. 1977. Vol. 197 P. 287.
Received: 
29.04.2016
Accepted: 
13.05.2016
Published: 
30.06.2016
Short text (in English):
(downloads: 109)