Stochastic exponential synchronization control of memristive neural networks with multiple time-varying delays

As an indispensable part of memristive synaptic weights, the switching jumps can induce instability, oscillation or even chaos to the memristive network system. Based on the available information of the switching jumps, this paper is concerned with the stochastic exponential synchronization of a class of memristive neural networks with multiple time-varying delays. By using stochastic differential inclusions and Lyapunov stability theory, discontinuous state feedback controller which depends upon the switching jumps is proposed. Compared with the previous state feedback scheme, more information of memristive synaptic weights is used to design the synchronous controller which ensures the stochastic exponential synchronization of considered networks. When the information of switching jumps is incomplete, discontinuous adaptive controller which is independent of the switching jumps is also designed, thus the applicability of synchronization is broadened. A numerical example is provided to illustrate the effectiveness and potential of the proposed design techniques.