Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network - He, Shaobo (Frontiers in Applied Mathematics and Statistics, 2020)
At present, dynamics and coupled control of fractional-order nonlinear systems have aroused many interests of researchers. In this paper, the fractional-order derivative is introduced to an improved memristor neural system. Dynamics of the fractional-order memristor neural model is investigated by means of bifurcation diagram, Lyapunov exponents and phase diagrams. To discuss about the dynamical behavior of the fractional-order memristor neuron in a network, we construct a ring network of neurons and capture the spatiotemporal patterns of the neuron in the network in the presence of different excitations. Finally, the chimera state is observed and complexity of the network is analyzed. It shows that complexity algorithm provides a new approach for the dynamical analysis of the network.