Delay-dependent performance state estimation of static delayed neural networks using sampled-data control

In this paper, we consider the issue of delay-dependent performance state estimation of static delayed neural networks using sampled-data control. A sensible Lyapunov-Krasovskii functional with triple and quadruplex integral terms is constructed. By using Jensen's inequality, Wirtinger-based inequality, and reciprocally convex technique, the stability conditions are derived. Delay-dependent criterion is acquired under which the estimation error framework is asymptotically stable with an endorsed performance. Instead of the continuous measurement, the sampled measurement is employed to estimate the neuron states. It is further demonstrated that the configuration of the gain matrix of state estimator is changed to find a feasible solution of a linear matrix inequalities, which is efficiently facilitated by available algorithms. At last, numerical cases are incorporated to demonstrate that the proposed technique is less moderate than existing ones.