Sampled-data state estimation for delayed memristive neural networks with reaction-diffusion terms: Hardy-Poincarè inequality

The purpose of this paper is to design a sampled-data state estimator to better estimate the delayed reaction-diffusion memristive neural networks. To tackle with the effect caused by the reaction-diffusion terms, a new agency of Hardy-Poincarè inequality was introduced, which proposed a more accurate estimation. In addition, based on Lyapunov function, robust analysis method, some brand-new solvability criteria are presented, which rest upon the size of the delays, the sampling period as well as the regional feature of the reaction-diffusion region. Finally, two numerical examples are exploited to show the effectiveness of the derived LMI-based conditions.