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Field coupling benefits signal exchange between Colpitts systems

Author

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  • Ren, Guodong
  • Xue, Yuxiong
  • Li, Yuwei
  • Ma, Jun

Abstract

Some evidences have confirmed that field coupling is much effective to realize signal propagation between neurons, and the biological function of synapse connection has also been modulated when field coupling is activated. These theoretical prediction and confirmation are approached on neuron model with electromagnetic induction and magnetic flux coupling is used to describe the effect of field coupling. Neuron is treated as a smart signal processor and neuronal activities can be reproduced in electric circuit by setting appropriate parameters. When time-varying current flows along the inductorium, magnetic flux across the coil is changed and induced electromotive force of the inductor is triggered. Indeed, exchange of magnetic flux between inductoriums (induction coils) can trigger modulation on magnetic field. Therefore, two nonlinear circuits can be connected to reach possible consensus of outputs by using this kind of field coupling. In this paper, two identical Colpitts oscillators are coupled by transformer which is introduced from partial inductance equivalent circuit (PEEC), and the potential differences between circuit nodes are analysed to find synchronization approach under field coupling. An unit matrix is used to derive the Master Stability Functions of the coupled systems, and the synchronization manifold of the system describes the effect of the parasitic elements on dynamical behaviour. It is also found that both of the gain of the oscillators and the coupling coefficient of transformer are important bifurcation parameters for synchronization manifold of the system. Similar investigation is as well practiced on printed circuit board (PCB) and the synchronization approach is confirmed under field coupling. This kind of field coupling provides another effective way to synchronization modulation via continuous exchange of field energy in the coupling device.

Suggested Citation

  • Ren, Guodong & Xue, Yuxiong & Li, Yuwei & Ma, Jun, 2019. "Field coupling benefits signal exchange between Colpitts systems," Applied Mathematics and Computation, Elsevier, vol. 342(C), pages 45-54.
    • Handle: RePEc:eee:apmaco:v:342:y:2019:i:c:p:45-54
    DOI: 10.1016/j.amc.2018.09.017
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    1. Liu, Zhilong & Zhou, Ping & Ma, Jun & Hobiny, Aatef & Alzahrani, Faris, 2020. "Autonomic learning via saturation gain method, and synchronization between neurons," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    2. Wu, Fuqiang & Kang, Ting & Shao, Yan & Wang, Qingyun, 2023. "Stability of Hopfield neural network with resistive and magnetic coupling," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    3. Liu, Zhilong & Ma, Jun & Zhang, Ge & Zhang, Yin, 2019. "Synchronization control between two Chua′s circuits via capacitive coupling," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 94-106.
    4. Guo, Yeye & Wang, Chunni & Yao, Zhao & Xu, Ying, 2022. "Desynchronization of thermosensitive neurons by using energy pumping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    5. Wu, Fuqiang & Guo, Yitong & Ma, Jun & Jin, Wuyin, 2023. "Synchronization of bursting memristive Josephson junctions via resistive and magnetic coupling," Applied Mathematics and Computation, Elsevier, vol. 455(C).
    6. Wafo Tekam, Raoul Blaise & Kengne, Jacques & Djuidje Kenmoe, Germaine, 2019. "High frequency Colpitts’ oscillator: A simple configuration for chaos generation," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 351-360.

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