Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities

In this paper, we formulate and investigate the impulsive synchronization of memristor based bidirectional associative memory (BAM) neural networks with time varying delays. Based on the linear matrix inequality (LMI) approach, the impulsive time dependent results are derived for the exponential stability of the error system, which guarantees the exponential synchronization of the BAM model by means of master–slave synchronization concept. Different from the existing models, an observer (slave system) for the considered BAM neural network in this paper is modeled with time-varying and random impulse moments. Some sufficient conditions are obtained to guarantee the exponential synchronization of the BAM model is derived by using the time-varying Lyapunov function. Simple LMI expressions are proposed to find the feedback controller gains at impulse instants. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results.