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