Exponential convergence rate estimation for uncertain delayed neural networks of neutral type

The global exponential stability for a class of uncertain delayed neural networks (DNNs) of neutral type is investigated in this paper. Delay-dependent and delay-independent criteria are proposed to guarantee the robust stability of DNNs via LMI and Razumikhin-like approaches. For a given delay, the maximal allowable exponential convergence rate will be estimated. Some numerical examples are given to illustrate the effectiveness of our results. The simulation results reveal significant improvement over the recent results.     

Magnetic Kronig-Penney-type graphene superlattices: finite energy Dirac points with anisotropic velocity renormalization

We study the energy band structure of magnetic graphene superlattices with delta-function magnetic barriers and zero average magnetic field. The dispersion relation obtained using the T-matrix approach shows the emergence of an infinite number of Dirac-like points at finite energies, while the original Dirac point is still located at the same place as that for pristine graphene. The carrier group velocity at the original Dirac point is isotropically renormalized, but at finite energy Dirac points it is generally anisotropic. An asymmetry in the width between the wells and the barriers of the periodic potential induces a shift of the original Dirac point in the zero-energy plane, keeping the velocity renormalization isotropic.