Symmetrical coexisting attractors and extreme multistability induced by memristor operating configurations in SC-CNN

Abstract Recently, a striking dynamical phenomenon characterized as coexisting attractors in nonlinear systems has been extensively studied. In this study, a novel memristive chaotic system is proposed by introducing a universal memristor emulator into a state controlled cellular neural network (SC-CNN), in which the emulator can be configured as an incremental or a decremental memristor. When the system parameter is varied, interestingly, a series of symmetrical attractors are induced by interchanging the operating configurations of the used emulator in the presented system. Furthermore, the dynamical behavior depending on the memristor initial values is also investigated and a more complex phenomenon of the symmetrical extreme multistability is revealed. The complex dynamical characteristics of the system are investigated by phase portraits, bifurcation diagrams, Lyapunov exponents. Pspice simulations and hardware experiments are included to verify numerical simulations.