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Energy balance between two thermosensitive circuits under field coupling

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Abstract

A thermistor is connected to the induction coil in series of the Chua circuit and a thermosensitive oscillator is obtained by applying scale transformation on the physical variables and parameters in the circuit equations. The dynamics dependence on temperature and synchronization stability under field coupling are discussed in detail. The sole Hamilton energy for each thermosensitive oscillator is calculated, and energy pumping along the coupling channel is continued before reaching complete synchronization between two thermosensitive circuits. The energy propagation in the coupling channel is controlled by the physical property of electric components such as capacitor and inductor. The magnetic field energy (or electric field energy) is pumped across the coupling channel when induction coil (or capacitor) is used to activate field coupling between chaotic circuits. Within this work, capacitor, and induction coil are respectively used to synchronize two thermosensitive circuits and the energy balance is also controlled. When two identical thermosensitive oscillators are controlled adaptively to reach complete synchronization, the energy balance is also realized. Furthermore, the temperature parameter is changed to detect the synchronization stability, and energy pumping along the coupling components in the coupling channel is estimated for detecting complete synchronization approach. Analog circuit implement is verified via Multisim and synchronization realization is detected as well. The advantage of this coupling scheme is that energy is pumped rather than consuming the Joule heat in the coupling channel before reaching complete synchronization, and the coupling channel can also be controlled by external physical field. The involvement of thermistor makes the chaotic systems become sensitive to the temperature, while the coupling scheme still works well and energy balance is controlled for reaching complete synchronization. Finally, a section for exploring open problems is supplied to guide the realization of adaptive synchronization control and energy balance for other nonlinear systems and neural circuits.

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The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Crutchfield, J.P.: Between order and chaos. Nat. Phys. 8, 17–24 (2012)

    Google Scholar 

  2. Braiman, Y., Lindner, J.F., Ditto, W.L.: Taming spatiotemporal chaos with disorder. Nature 378(6556), 465–467 (1995)

    Google Scholar 

  3. Skinner, J.E., Molnar, M., Vybiral, T., et al.: Application of chaos theory to biology and medicine. Integr. Physiol. Behav. Sci. 27(1), 39–53 (1992)

    Google Scholar 

  4. Barton, S.: Chaos, self-organization, and psychology. Am. Psychol. 49(1), 5 (1994)

    Google Scholar 

  5. Petrovskii, S., Li, B.L., Malchow, H.: Quantification of the spatial aspect of chaotic dynamics in biological and chemical systems. Bull. Math. Biol. 65, 425–446 (2003)

    MATH  Google Scholar 

  6. Simic, M., Babic, Z., Risojevic, V., et al.: Non-iterative parameter estimation of the 2R–1C model suitable for low-cost embedded hardware. Front. Inf. Technol. Electron. Eng. 21, 476–490 (2020)

    Google Scholar 

  7. Li, Y.H., Zheng, W.J., et al.: Aircraft safety analysis based on differential manifold theory and bifurcation method. Front. Inf. Technol. Electron. Eng. 20, 292–299 (2019)

    Google Scholar 

  8. Wessel, N., Voss, A., Malberg, H., et al.: Nonlinear analysis of complex phenomena in cardiological data. Herzschrittmachertherapie und Elektrophysiologie 11, 159–173 (2000)

    Google Scholar 

  9. Buscarino, A., Fortuna, L., Frasca, M., et al.: A chaotic circuit based on Hewlett–Packard memristor. Chaos 22, 023136 (2012)

    MathSciNet  MATH  Google Scholar 

  10. Li, C., Thio, W.J.C., Sprott, J.C., et al.: Constructing infinitely many attractors in a programmable chaotic circuit. IEEE Access 6, 29003–29012 (2018)

    Google Scholar 

  11. Wang, N., Zhang, G.S., Bao, H., et al.: Bursting oscillations and coexisting attractors in a simple memristor-capacitor-based chaotic circuit. Nonlinear Dyn. 97, 1477–1494 (2019)

    MATH  Google Scholar 

  12. Chen, M., Qi, J.W., Wu, H.G., et al.: Bifurcation analyses and hardware experiments for bursting dynamics in non-autonomous memristive FitzHugh-Nagumo circuit. Sci. China Technol. Sci. 63, 1035–1044 (2020)

    Google Scholar 

  13. Sprott, J.C., Thio, W.J.: A chaotic circuit for producing Gaussian random numbers. Int. J. Bifurcation Chaos 30, 2050116 (2020)

    MathSciNet  MATH  Google Scholar 

  14. Xu, L., Huang, G., Chen, Q.L., et al.: An improved method for image denoising based on fractional-order integration. Front. Inf. Technol. Electron. Eng. 21, 1485–1493 (2020)

    Google Scholar 

  15. Chen, L., Yin, H., Yuan, L., et al.: A novel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNA sequence operations. Front. Inf. Technol. Electron. Eng. 21, 866–879 (2020)

    Google Scholar 

  16. Wang, Z.R., Shi, B., Baleanu, D.: Discrete fractional watermark technique. Front. Inf. Technol. Electron. Eng. 21, 880–883 (2020)

    Google Scholar 

  17. Xiao, D., Wang, Y., Xiang, T., et al.: High-payload completely reversible data hiding in encrypted images by an interpolation technique. Front. Inf. Technol. Electron. Eng. 18, 1732–1743 (2020)

    Google Scholar 

  18. Naskar, P.K., Bhattacharyya, S., Nandy, D., et al.: A robust image encryption scheme using chaotic tent map and cellular automata. Nonlinear Dyn. 100, 2877–2898 (2020)

    Google Scholar 

  19. Bao, B., Zhu, Y., Li, C., et al.: Global multistability and analog circuit implementation of an adapting synapse-based neuron model. Nonlinear Dyn. 101, 1105–1118 (2020)

    Google Scholar 

  20. Lin, H.R., Wang, C.H., Sun, Y.C., et al.: Firing multistability in a locally active memristive neuron model. Nonlinear Dyn. 100, 3667–3683 (2020)

    Google Scholar 

  21. Hu, X.Y., Liu, C.X.: Dynamic property analysis and circuit implementation of simplified memristive Hodgkin-Huxley neuron model. Nonlinear Dyn. 97, 1721–1733 (2019)

    MATH  Google Scholar 

  22. Feali, M.S., Ahmadi, A., Hayati, M.: Implementation of adaptive neuron based on memristor and memcapacitor emulators. Neurocomputing 309, 157–167 (2018)

    Google Scholar 

  23. Xu, Y., Guo, Y.Y., Ren, G.D., et al.: Dynamics and stochastic resonance in a thermosensitive neuron. Appl. Math. Comput. 385, 125427 (2020)

    MathSciNet  MATH  Google Scholar 

  24. Liu, Y., Xu, W., Ma, J., et al.: A new photosensitive neuron model and its dynamics. Front. Inf. Technol. Electron. Eng. 21, 87–1396 (2020)

    Google Scholar 

  25. Xu, Y., Liu, M., Zhu, Z., et al.: Dynamics and coherence resonance in a thermosensitive neuron driven by photocurrent. Chin. Phys. B 29, 098704 (2020)

    Google Scholar 

  26. Yao, Z., Zhou, P., Zhu, Z., et al.: Phase synchronization between a light-dependent neuron and a thermosensitive neuron. Neurocomputing 423, 518–534 (2021)

    Google Scholar 

  27. Zhang, Y., Xu, Y., Yao, Z., et al.: A feasible neuron for estimating the magnetic field effect. Nonlinear Dyn. 102, 1849–1867 (2020)

    Google Scholar 

  28. Zhang, Y., Wang, C., Tang, J., et al.: Phase coupling synchronization of FHN neurons connected by a Josephson junction. Sci. China Technol. Sci. 63, 2328–2338 (2020)

    Google Scholar 

  29. Liu, Z., Wang, C., Jin, W., et al.: Capacitor coupling induces synchronization between neural circuits. Nonlinear Dyn. 97, 2661–2673 (2019)

    MATH  Google Scholar 

  30. Xu, Y.M., Yao, Z., Hobiny, A., et al.: Differential coupling contributes to synchronization via a capacitor connection between chaotic circuits. Front. Inf. Technol. Electron. Eng. 20, 571–583 (2019)

    Google Scholar 

  31. Yao, Z., Ma, J., Yao, Y.G., et al.: Synchronization realization between two nonlinear circuits via an induction coil coupling. Nonlinear Dyn. 96, 205–217 (2019)

    MATH  Google Scholar 

  32. Wu, F., Ma, J., Zhang, G.: Energy estimation and coupling synchronization between biophysical neurons. Sci. China Technol. Sci. 63, 625–636 (2020)

    Google Scholar 

  33. Wang, C., Tang, J., Ma, J.: Minireview on signal exchange between nonlinear circuits and neurons via field coupling. Eur. Phys. J. Spec. Topics 228, 1907–1924 (2019)

    Google Scholar 

  34. Liu, Z., Zhou, P., Ma, J., et al.: Autonomic learning via saturation gain method, and synchronization between neurons. Chaos Solitons Fractals 131, 109533 (2020)

    MathSciNet  MATH  Google Scholar 

  35. Ma, J., Yang, Z., Yang, L., et al.: A physical view of computational neurodynamics. J. Zhejiang Univ.-Sci. A 20, 639–659 (2019)

    Google Scholar 

  36. Chen, M., Feng, Y., Bao, H., et al.: State variable mapping method for studying initial-dependent dynamics in memristive hyper-jerk system with line equilibrium. Chaos Solitons Fractals 115, 313–324 (2018)

    MathSciNet  Google Scholar 

  37. Wu, H., Ye, Y., Bao, B., et al.: Memristor initial boosting behaviors in a two-memristor-based hyperchaotic system. Chaos Solitons Fractals 121, 178–185 (2019)

    MathSciNet  MATH  Google Scholar 

  38. Bao, B., Li, H.Z., Zhu, L., et al.: Initial-switched boosting bifurcations in 2D hyperchaotic map. Chaos 30, 033107 (2020)

    MathSciNet  MATH  Google Scholar 

  39. Chen, M., Ren, X., Wu, H., et al.: Periodically varied initial offset boosting behaviors in a memristive system with cosine memductance. Front. Inf. Technol. Electron. Eng. 20, 1706–1716 (2019)

    Google Scholar 

  40. Wu, F., Zhou, P., Alsaedi, A., et al.: Synchronization dependence on initial setting of chaotic systems without equilibria. Chaos Solitons Fractals 110, 124–132 (2018)

    MathSciNet  Google Scholar 

  41. Wu, F., Ma, J., Ren, G., et al.: Synchronization stability between initial-dependent oscillators with periodical and chaotic oscillation. J. Zhejiang Univ.-Sci. A 19, 889–903 (2018)

    Google Scholar 

  42. Ma, J., Wu, F., Wang, C.: Synchronization behaviors of coupled neurons under electromagnetic radiation. Int. J. Mod. Phys. B 31, 1650251 (2017)

    MathSciNet  Google Scholar 

  43. Ma, J., Xu, W., Zhou, P., et al.: Synchronization between memristive and initial-dependent oscillators driven by noise. Physica A 536, 22598 (2019)

    MathSciNet  MATH  Google Scholar 

  44. Chua, L.O.: Chua’s circuit: an overview ten years later. J. Circuits Syst. Comput. 4, 117–159 (1994)

    Google Scholar 

  45. Chua, L.O.: The genesis of Chua’s circuit. ArchivfürElektronik und Ubertragung-stechnik 46, 250–257 (1992)

    Google Scholar 

  46. Sarasola, C., Torrealdea, F.J., d’Anjou, A., et al.: Energy balance in feedback synchronization of chaotic systems. Phys. Rev. E 69, 011606 (2004)

    Google Scholar 

  47. Torrealdea, F.J., d’Anjou, A., Graña, M.: Energy aspects of the synchronization of model neurons. Phys. Rev. E 74, 011905 (2006)

    Google Scholar 

  48. Zhang, G., Wang, C.N., Alsaedi, A., et al.: Dependence of hidden attractors on non-linearity and Hamilton energy. Kybernetika 54, 648–663 (2018)

    MathSciNet  MATH  Google Scholar 

  49. Xiao, Y., Zhu, K., Liaw, H.C.: Generalized synchronization control of multi-axis motion systems. Control. Eng. Pract. 13, 809–819 (2005)

    Google Scholar 

  50. Park, J.H.: Adaptive synchronization of Rössler system with uncertain parameters. Chaos Solitons Fractals 25, 333–338 (2005)

    MATH  Google Scholar 

  51. Sajjadi, S.S., Baleanu, D., Jajarmi, A., et al.: A new adaptive synchronization and hyperchaos control of a biological snap oscillator. Chaos Solitons Fractals 138, 109919 (2020)

    MathSciNet  MATH  Google Scholar 

  52. Elabbasy, E.M., Agiza, H.N., El-Dessoky, M.M.: Adaptive synchronization of a hyperchaotic system with uncertain parameter. Chaos Solitons Fractals 30, 1133–1142 (2006)

    MathSciNet  MATH  Google Scholar 

  53. Bowong, S.: Adaptive synchronization between two different chaotic dynamical systems. Commun. Nonlinear Sci. Numer. Simul. 12, 976–985 (2007)

    MathSciNet  MATH  Google Scholar 

  54. Zhou, P., Hu, X.K., Zhu, Z., et al.: What is the most suitable Lyapunov function? Chaos Solitons Fractals 150, 111154 (2021)

    MathSciNet  MATH  Google Scholar 

  55. Arqub, O.A., Hayat, T., Alhodaly, M.: Analysis of lie symmetry, explicit series solutions, and conservation laws for the nonlinear time-fractional phi-four equation in two-dimensional space. Int. J. Appl. Comput. Math. 8, 145 (2022)

    MathSciNet  MATH  Google Scholar 

  56. Beghami, W., Maayah, B., Bushnaq, S., et al.: The Laplace optimized decomposition method for solving systems of partial differential equations of fractional order. Int. J. Appl. Comput. Math. 8, 52 (2022)

    MathSciNet  Google Scholar 

  57. Momani, S., Abu Arqub, O., Maayah, B.: Piecewise optimal fractional reproducing kernel solution and convergence analysis for the Atangana–Baleanu–Caputo model of the Lienard’s equation. Fractals 28(08), 2040007 (2020)

    MATH  Google Scholar 

  58. Momani, S., Maayah, B., Arqub, O.A.: The reproducing kernel algorithm for numerical solution of Van der Pol damping model in view of the Atangana-Baleanu fractional approach. Fractals 28(8), 2040010 (2020)

    MATH  Google Scholar 

  59. Chen, J.X., Zhan, S., Qiao, L.Y., et al.: Collective dynamics of self-propelled nanomotors in chemically oscillating media. EPL 125, 26002 (2019)

    Google Scholar 

  60. Chen, J.X., Xiao, J.H., Qiao, L.Y., et al.: Dynamics of scroll waves with time-delay propagation in excitable media. Commun. Nonlinear Sci. Numer. Simul. 59, 331–337 (2018)

    MathSciNet  MATH  Google Scholar 

  61. Zhou, P., Zhang, X.F., Ma, J.: How to wake up the electric synapse coupling between neurons? Nonlinear Dyn. 108, 1681–1695 (2022)

    Google Scholar 

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Acknowledgements

This project is supported by Gansu Natural Science Foundation under Grant No. 20JR5RA473. The authors thank Dr. Jun Ma for this checking and improvement in the writing.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Xiufang Zhang and Xikui Hu validated the model approach and numerical results. Ping Zhou confirmed the experimental results, and edited the draft. Guodong Ren suggested this project, validated the proof and wrote this draft.

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Correspondence to Guodong Ren.

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Zhou, P., Zhang, X., Hu, X. et al. Energy balance between two thermosensitive circuits under field coupling. Nonlinear Dyn 110, 1879–1895 (2022). https://doi.org/10.1007/s11071-022-07669-z

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  • DOI: https://doi.org/10.1007/s11071-022-07669-z

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