Skip to main content
Log in

Chaos anti-control of coexisting infinite signals and pinning synchronization of a complex-valued laser chain network

  • Regular Article
  • Published:
The European Physical Journal Plus Aims and scope Submit manuscript

Abstract

There are many applications of chaos anti-control from its inherent state to the expected chaotic state in different disciplines ranging from engineering to biology and social sciences. This paper studies the dynamical characteristics, chaos anti-control of coexisting attractors, and pinning synchronization of a complex-valued laser chain network (CVLCN). Firstly, a CVLCN based on the complex-valued Lorenz laser systems is developed, and detailed dynamic analyses for the important parameters are executed to obtain significant results of the hyper-chaotic attractors and quasi-periodic attractors, which are confirmed by phase bifurcation diagram, Lyapunov exponent spectrum, the Kaplan–Yorke dimension, and power spectrum. A key point of this paper is the discovery of the coexistence of infinite hyper-chaotic attractors and quasi-periodic attractors. Given the vital role of oscillations and network dynamics in neural processes related to health and disease, the presence of quasi-periodic signals may indicate an irregular brain state. Therefore, a suitable controller is designed to realize intermittent and non-intermittent chaos anti-control of the coexisting infinite quasi-periodic signals. Finally, the pinning controller is designed to achieve the network synchronization by the theoretical and numerical simulation research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availibility

This manuscript has associated data in a data repository. [Authors' comment: The data that support the findings of this study are available from the corresponding author upon reasonable request.]

References

  1. E.N. Lorenz, Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130–141 (1963)

    Article  ADS  MathSciNet  Google Scholar 

  2. H. Haken, Analogy between higher instabilities in fluids and lasers. Phys. Lett. A 53(1), 77–78 (1975)

    Article  ADS  Google Scholar 

  3. A.C. Fowler, J.D. Gibbon, M.J. McGuinness, The complex Lorenz equations. Phys. D 4(2), 139–163 (1982)

    Article  MathSciNet  Google Scholar 

  4. S. Wieczorek, B. Krauskopf, D. Lenstra, Sudden chaotic transitions in an optically injected semiconductor laser. Opt. Lett. 26(11), 816–818 (2001)

    Article  ADS  Google Scholar 

  5. P.R. Prucnal, B.J. Shastri, T.F. DeLima, Recent progress in semiconductor excitable lasers for photonic spike processing. Adv. Opt. Photon. 8(2), 228–299 (2016)

    Article  Google Scholar 

  6. X. Zhao, J. Liu, J. Mou, Characteristics of a laser system in complex field and its complex self-synchronization. Eur. Phys. J. Plus 135, 1–17 (2020)

    Article  ADS  Google Scholar 

  7. Y.Q. Li, J. Liu, C.B. Li, Z.F. Hao, X.T. Zhang, Dynamical analysis, geometric control and digital hardware implementation of a complex-valued laser system with a locally active memristor. Chin. Phys. B 32, 080503 (2023)

    Article  ADS  Google Scholar 

  8. F.F. Zhang, X. Zhang, M.Y. Cao, F.Y. Ma, Z.F. Li, Characteristic analysis of 2D lag-complex logistic map and its application in image encryption. IEEE Multim. 28(4), 96–106 (2021)

    Article  Google Scholar 

  9. J.Y. Wu, C.B. Li, Y. Xu, Y.C. Jiang, A triode-based analog gate and its application in chaotic circuits. IEEE Trans. Circ. Syst. I Regul. Pap. 70(1), 378–387 (2022)

    Article  ADS  Google Scholar 

  10. F.F. Yang, G.D. Ren, J. Tang, Dynamics in a memristive neuron under an electromagnetic field. Nonlinear Dyn. 111, 21917–21939 (2023)

    Article  Google Scholar 

  11. Y.W. Sha, J. Mou, S. Banerjee, Y.S. Zhang, Exploiting flexible and secure cryptographic technique for multi-dimensional image based on graph data structure and three-input majority gate. IEEE Trans. Indus. Inf. (2023). https://doi.org/10.1109/TII.2023.3281659

    Article  Google Scholar 

  12. M.J. Zhang, Y.N. Ji, Y.N. Zhang, Y. Wu, H. Xu, W.P. Xu, Remote radar based on chaos generation and radio over fiber. IEEE Photon. J. 6(5), 1–12 (2014)

    Article  ADS  Google Scholar 

  13. M. Bahadoran, P. Yupapin, All-optical notch filters for ultra-wideband chaotic communications. Eur. Phys. J. Plus 133(11), 487 (2018)

    Article  Google Scholar 

  14. J. Robertson, P. Kirkland, J.A. Alanis, M. Hejda, J. Bueno, G.D. Caterina, A. Hurtado, Ultrafast neuromorphic photonic image processing with a VCSEL neuron. Sci. Rep. 12(1), 4874 (2022)

    Article  ADS  Google Scholar 

  15. R. Kumar, A.P. delPino, S. Sahoo, R.K. Singh, W.K. Tan, K. Kar, A. Matsuda, E. Joanni, Laser processing of graphene and related materials for energy storage: State of the art and future prospects. Progr. Energy Combus. Sci. 91, 100981 (2022)

    Article  Google Scholar 

  16. M.C. Soriano et al., Complex photonics: Dynamics and applications of delay-coupled semiconductors lasers. Rev. Mod. Phys. 85(1), 421 (2013)

    Article  ADS  Google Scholar 

  17. H.M. Kondo, D. Farkas, S.L. Denham, T. Asai, I. Winkler, Auditory multistability and neurotransmitter concentrations in the human brain. Philos. Trans. R. Soc. B Biol. Sci. 372(1714), 20160110 (2017)

    Article  Google Scholar 

  18. M. Chen, D. Veeman, Z. Wang, A. Karthikeyan, Chimera states in a network of identical oscillators with symmetric coexisting attractors. Eur. Phys. J. Spec. Topics 231(11–12), 2163–2171 (2022)

    Article  ADS  Google Scholar 

  19. H. Bao, M.J. Hua, J. Ma, M. Chen, B.C. Bao, Offset-control plane coexisting behaviors in two-memristor-based Hopfield neural network. IEEE Trans. Indus. Electr. 70(10), 10526–10535 (2022)

    Article  Google Scholar 

  20. H.T. Qiu, X.M. Xu, Z.H. Jiang, K.H. Sun, C. Cao, Dynamical behaviors, circuit design, and synchronization of a novel symmetric chaotic system with coexisting attractors. Sci. Rep. 13(1), 1893 (2023)

    Article  ADS  Google Scholar 

  21. X.C. Liu, Q. Tu, Coexisting and hidden attractors of memristive chaotic systems with and without equilibria. Eur. Phys. J. Plus 137(4), 516 (2022)

    Article  Google Scholar 

  22. Q. Lai, L. Yang, Discrete memristor applied to construct neural networks with homogeneous and heterogeneous coexisting attractors. Chaos Solit. Fract. 174, 113807 (2023)

    Article  MathSciNet  Google Scholar 

  23. C.B. Li, Z.N. Li, Y.C. Jiang, T.F. Lei, W. Xiong, Symmetric strange attractors: a review of symmetry and conditional symmetry. Symmetry 15(8), 1564 (2023)

    Article  ADS  Google Scholar 

  24. Hebb, D.O.: The Organization of Behavior: a Neuropsychoblogical Theory. Psychology Press, (2005)

  25. E.C.W. VanStraaten, C.J. Stam, Structure out of chaos: functional brain network analysis with EEG, MEG, and functional MRI. Eur. Neuropsychopharmacol. 23(1), 7–18 (2013)

    Article  Google Scholar 

  26. W.J. Freeman, Strange attractors that govern mammalian brain dynamics shown by trajectories of electroencephalographic (EEG) potential. IEEE Trans. Circ. Syst. 35(7), 781–783 (1988)

    Article  MathSciNet  Google Scholar 

  27. S. Panahi, Z. Aram, S. Jafari, J. Ma, J.C. Sprott, Modeling of epilepsy based on chaotic artificial neural network. Chaos Solit. Fract. 105, 150–156 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  28. J.H. Lin, C. Chen, H.Y. Shyu, S.Y. Kwan, C.H. Yiu, Stimulus-induced rhythmic, periodic, or ictal discharges. Clin. Neurol. Neurosurg. 114(9), 1283–1286 (2012)

    Article  Google Scholar 

  29. T. Shinbrot, E. Ott, C. Grebogi, J.A. Yorke, Using chaos to direct trajectories to targets. Phys. Rev. Lett. 65(26), 3215 (1990)

    Article  ADS  Google Scholar 

  30. Y.X. Li, C.B. Li, S.C. Liu, Z.Y. Hua, H.B. Jiang, A 2-D conditional symmetric hyperchaotic map with complete control. Nonlinear Dyn. 109(2), 1155–1165 (2022)

    Article  Google Scholar 

  31. G.R. Chen, What does chaos have to do with systems and control engineering? J. Syst. Sci. Complex. 14(1), 31 (2001)

    ADS  MathSciNet  Google Scholar 

  32. M. Borah, D. Das, A. Gayan, F. Fenton, E. Cherry, Control and anticontrol of chaos in fractional-order models of Diabetes, HIV, Dengue, Migraine, Parkinson’s and Ebola virus diseases. Chaos Solit. Fract. 153, 111419 (2021)

    Article  MathSciNet  Google Scholar 

  33. S. Raiesdana, S.M.H. Goplayegani, Study on chaos anti-control for hippocampal models of epilepsy. Neurocomputing 111, 54–69 (2013)

    Article  Google Scholar 

  34. Y. Han, J. Ding, L. Du, Y. Lei, Control and anti-control of chaos based on the moving largest Lyapunov exponent using reinforcement learning. Phys. D Nonlinear Phenom. 428, 133068 (2021)

    Article  Google Scholar 

  35. H.K. Chen, C.I. Lee, Anti-control of chaos in rigid body motion. Chaos Solit. Fract. 21(4), 957–965 (2004)

    Article  ADS  MathSciNet  Google Scholar 

  36. E. Zhu, M. Xu, D. Pi, Anti-control of Hopf bifurcation for high-dimensional chaotic system with coexisting attractors. Nonlinear Dyn. 110(2), 1867–1877 (2022)

    Article  Google Scholar 

  37. X.T. Zhang, J. Liu, D. Wang, H.J. Liu, Geometric control and synchronization of a complex-valued laser chain network. Nonlinear Dyn. 111(7), 6395–6410 (2023)

    Article  Google Scholar 

  38. X. Zhao, J. Liu, G.R. Chen, L. Chai, D. Wang, Dynamics and synchronization of complex-valued ring networks. Int. J. Bifurcat. Chaos 32(05), 2230011 (2022)

    Article  MathSciNet  Google Scholar 

  39. Z. Yao, K.H. Sun, S.B. He, Synchronization in fractional-order neural networks by the energy balance strategy. Cogn. Neurodyn. 2, 1–13 (2023)

    Google Scholar 

  40. L. Chai, J. Liu, G.R. Chen, X. Zhao, Dynamics and synchronization of a complex-valued star network. Sci. China Technol. Sci. 64(12), 2729–2743 (2021)

    Article  ADS  Google Scholar 

  41. J. Liu, G.R. Chen, X. Zhao, Generalized synchronization and parameters identification of different-dimensional chaotic systems in the complex field. Fractals 29(04), 2150081 (2021)

    Article  ADS  Google Scholar 

  42. C.X. Shang, K.H. Sun, H.H. Wang, Y. Zhao, S.B. He, Spatial patterns and chimera states in discrete memristor coupled neural networks. Nonlinear Dyn. 111(21), 20347–20360 (2023)

    Article  Google Scholar 

  43. J.C. Liang, J. Liu, G.R. Chen, Observer-based synchronization of time-delay complex-variable chaotic systems with complex parameters. Fractals 30(09), 2250197 (2022)

    Article  ADS  Google Scholar 

  44. L. Chai, J. Liu, G.R. Chen, X.T. Zhang, Y.Q. Li, Amplitude control and chaotic synchronization of a complex-valued laser ring network. Fractals 7(31), 2350091 (2023)

    Article  Google Scholar 

  45. M. Gerster, R. Berner, J. Sawicki, A. Zakharova, A. Škoch, J. Hlinka, E. Schöll, FitzHugh-Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena. Chaos 30, 12 (2020)

    Article  MathSciNet  Google Scholar 

  46. M. Abeles, Y. Prut, H. Bergman, E. Vaadia, Synchronization in neuronal transmission and its importance for information processing. Progr. Brain Res. 102, 395–404 (1994)

    Article  Google Scholar 

  47. G. Rigatos, Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter. Cogn. Neurodyn. 8, 465–478 (2014)

    Article  Google Scholar 

  48. Z.Y. Wu, X.C. Fu, Synchronization of complex-variable dynamical networks with complex coupling. Int. J. Mod. Phys. C 24(02), 1350007 (2013)

    Article  ADS  MathSciNet  Google Scholar 

  49. F.D. Kong, J.P. Sun, Pinning synchronization of complex dynamical networks on time scales. Int. J. Contr. Autom. Syst. 19, 878–888 (2021)

    Article  Google Scholar 

  50. J. Wang, X. Liu, Cluster synchronization for multi-weighted and directed complex networks via pinning control. IEEE Trans. Circ. Syst. II Express Briefs 69(3), 1347–1351 (2021)

    ADS  Google Scholar 

  51. G.R. Chen, Pinning control of complex dynamical networks. IEEE Trans. Consumer Electr. 68(4), 336–343 (2022)

    Article  MathSciNet  Google Scholar 

  52. Z.N. Tabekoueng, S.S. Muni, T.F. Fozin, G.D. Leutcho, J. Awrejcewicz, Coexistence of infinitely many patterns and their control in heterogeneous coupled neurons through a multistable memristive synapse. Chaos 32(5), 053114 (2022)

    Article  ADS  MathSciNet  Google Scholar 

  53. S. Panahi, Z. Aram, S. Jafari, J. Ma, J.C. Sprott, Modeling of epilepsy based on chaotic artificial neural network. Chaos Solit. Fract. 105, 150–156 (2017)

    Article  ADS  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Nature Science Foundation of China under Grant 61773010 and Taishan Scholar Foundation of Shandong Province ts20190938.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Liu, J., Liang, J. et al. Chaos anti-control of coexisting infinite signals and pinning synchronization of a complex-valued laser chain network. Eur. Phys. J. Plus 139, 65 (2024). https://doi.org/10.1140/epjp/s13360-023-04826-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1140/epjp/s13360-023-04826-0

Navigation