Intermittent sampled-data synchronization of delayed reaction–diffusion neural networks

This article investigates the synchronization of delayed reaction–diffusion neural networks (RDNNs) based on intermittent sampled-data (SD) control under spatially local averaged measurements (SLAMs), where distributed and discrete delays are introduced. In order to save the control cost and reduce communications burden, we introduce intermittent SD control based on SLAMs to design the synchronization scheme, which requires only a limited number of actuators and sensors. Meanwhile, we construct the interval-dependent Lyapunov functional (LF) for the synchronization error system, which is continuous during the sampling period. Then, according to the designed LF and several inequality techniques, some criteria for intermittent SD synchronization are established to ensure that the error system is exponentially stable. In addition, we transform the controller design issue into a feasibility problem of liner matrix inequalities (LMIs). In the end, two numeric examples are offered to ensure the feasibility and effectiveness of the method.