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
Crutchfield, J.P.: Between order and chaos. Nat. Phys. 8, 17–24 (2012)
Braiman, Y., Lindner, J.F., Ditto, W.L.: Taming spatiotemporal chaos with disorder. Nature 378(6556), 465–467 (1995)
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)
Barton, S.: Chaos, self-organization, and psychology. Am. Psychol. 49(1), 5 (1994)
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)
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)
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)
Wessel, N., Voss, A., Malberg, H., et al.: Nonlinear analysis of complex phenomena in cardiological data. Herzschrittmachertherapie und Elektrophysiologie 11, 159–173 (2000)
Buscarino, A., Fortuna, L., Frasca, M., et al.: A chaotic circuit based on Hewlett–Packard memristor. Chaos 22, 023136 (2012)
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)
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)
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)
Sprott, J.C., Thio, W.J.: A chaotic circuit for producing Gaussian random numbers. Int. J. Bifurcation Chaos 30, 2050116 (2020)
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)
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)
Wang, Z.R., Shi, B., Baleanu, D.: Discrete fractional watermark technique. Front. Inf. Technol. Electron. Eng. 21, 880–883 (2020)
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)
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)
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)
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)
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)
Feali, M.S., Ahmadi, A., Hayati, M.: Implementation of adaptive neuron based on memristor and memcapacitor emulators. Neurocomputing 309, 157–167 (2018)
Xu, Y., Guo, Y.Y., Ren, G.D., et al.: Dynamics and stochastic resonance in a thermosensitive neuron. Appl. Math. Comput. 385, 125427 (2020)
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)
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)
Yao, Z., Zhou, P., Zhu, Z., et al.: Phase synchronization between a light-dependent neuron and a thermosensitive neuron. Neurocomputing 423, 518–534 (2021)
Zhang, Y., Xu, Y., Yao, Z., et al.: A feasible neuron for estimating the magnetic field effect. Nonlinear Dyn. 102, 1849–1867 (2020)
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)
Liu, Z., Wang, C., Jin, W., et al.: Capacitor coupling induces synchronization between neural circuits. Nonlinear Dyn. 97, 2661–2673 (2019)
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)
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)
Wu, F., Ma, J., Zhang, G.: Energy estimation and coupling synchronization between biophysical neurons. Sci. China Technol. Sci. 63, 625–636 (2020)
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)
Liu, Z., Zhou, P., Ma, J., et al.: Autonomic learning via saturation gain method, and synchronization between neurons. Chaos Solitons Fractals 131, 109533 (2020)
Ma, J., Yang, Z., Yang, L., et al.: A physical view of computational neurodynamics. J. Zhejiang Univ.-Sci. A 20, 639–659 (2019)
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)
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)
Bao, B., Li, H.Z., Zhu, L., et al.: Initial-switched boosting bifurcations in 2D hyperchaotic map. Chaos 30, 033107 (2020)
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)
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)
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)
Ma, J., Wu, F., Wang, C.: Synchronization behaviors of coupled neurons under electromagnetic radiation. Int. J. Mod. Phys. B 31, 1650251 (2017)
Ma, J., Xu, W., Zhou, P., et al.: Synchronization between memristive and initial-dependent oscillators driven by noise. Physica A 536, 22598 (2019)
Chua, L.O.: Chua’s circuit: an overview ten years later. J. Circuits Syst. Comput. 4, 117–159 (1994)
Chua, L.O.: The genesis of Chua’s circuit. ArchivfürElektronik und Ubertragung-stechnik 46, 250–257 (1992)
Sarasola, C., Torrealdea, F.J., d’Anjou, A., et al.: Energy balance in feedback synchronization of chaotic systems. Phys. Rev. E 69, 011606 (2004)
Torrealdea, F.J., d’Anjou, A., Graña, M.: Energy aspects of the synchronization of model neurons. Phys. Rev. E 74, 011905 (2006)
Zhang, G., Wang, C.N., Alsaedi, A., et al.: Dependence of hidden attractors on non-linearity and Hamilton energy. Kybernetika 54, 648–663 (2018)
Xiao, Y., Zhu, K., Liaw, H.C.: Generalized synchronization control of multi-axis motion systems. Control. Eng. Pract. 13, 809–819 (2005)
Park, J.H.: Adaptive synchronization of Rössler system with uncertain parameters. Chaos Solitons Fractals 25, 333–338 (2005)
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)
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)
Bowong, S.: Adaptive synchronization between two different chaotic dynamical systems. Commun. Nonlinear Sci. Numer. Simul. 12, 976–985 (2007)
Zhou, P., Hu, X.K., Zhu, Z., et al.: What is the most suitable Lyapunov function? Chaos Solitons Fractals 150, 111154 (2021)
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)
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)
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)
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)
Chen, J.X., Zhan, S., Qiao, L.Y., et al.: Collective dynamics of self-propelled nanomotors in chemically oscillating media. EPL 125, 26002 (2019)
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)
Zhou, P., Zhang, X.F., Ma, J.: How to wake up the electric synapse coupling between neurons? Nonlinear Dyn. 108, 1681–1695 (2022)
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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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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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