Planar Homogeneous Coexisting Hyperchaos in Bi-Memristor Cyclic Hopfield Neural Network
Han Bao, Zhuguan Chen, Jun Ma, Quan Xu, Bocheng Bao · IEEE Transactions on Industrial Electronics · 2024
Memristors with synaptic plasticity can act as changeable connection weights. To address the issue of no chaos in cyclic trineuron Hopfield neural network with resistive weights, a bimemristor cyclic Hopfield neural network (BM-CHNN) is presented by substituting two resistive weights with two memristive weights, and thus chaos and hyperchaos are demonstrated. BM-CHNN has a planar equilibrium set, and the stability distribution related to two memristor initial states is discussed by exploring three nonzero eigenvalues. Further, parameter-relied bifurcation and heterogeneous coexisting behaviors are disclosed, and planar homogeneous coexisting hyperchaotic (HC) attractors regulated by the memristor initial states are uncovered. The results manifest that BM-CHNN not only displays chaos and hyperchaos but also exhibits the planar homogeneous coexisting hyperchaos owning the elegant basins of attraction with fantastic manifold structures and fractal boundaries. Finally, a STM32-based hardware platform is fabricated and the heterogeneous and homogeneous coexisting attractors are captured experimentally to confirm the numerical results.