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Circuit implementation and synchronization of dual-memristor heterogeneous cellular neural network with complex dynamics

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  • Ma, Tao
  • Mou, Jun
  • Chen, WanZhong

Abstract

Memristor-based cellular neural networks have demonstrated unique advantages in nonlinear dynamics. However, most existing models resort to homogeneous structures or single memristor coupling, which restricts the diversity and adaptability of system dynamical behaviors. To address this issue, a dual-memristor heterogeneous cellular neural network (DM-HetCNN) is proposed, where two memristors are incorporated into different feedback pathways to enhance dynamical complexity. The interaction between dual memristors and heterogeneous neurons gives rise to rich nonlinear phenomena, including brain-like chaos and extreme multistability. These features significantly enrich the dynamical structure and provide additional degrees of freedom for system evolution. Furthermore, a synchronization control scheme is developed to demonstrate its application potential. Finally, circuit and DSP implementation validate the practical realizability of the system, indicating its potential applications in neuromorphic computing and secure communications.

Suggested Citation

  • Ma, Tao & Mou, Jun & Chen, WanZhong, 2026. "Circuit implementation and synchronization of dual-memristor heterogeneous cellular neural network with complex dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 209(P1).
    • Handle: RePEc:eee:chsofr:v:209:y:2026:i:p1:s0960077926006636
    DOI: 10.1016/j.chaos.2026.118522
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