A New Result on H ∞ State Estimation for Delayed Neural Networks Based on an Extended Reciprocally Convex Inequality
Jinxing Hu, Guoqiang Tan, Lei Liu · IEEE Transactions on Circuits & Systems II Express Briefs · 2023
This brief investigates the$H_{\infty }$state estimation problem for neural networks with time-varying delay. First, an extended reciprocally convex inequality based on$r$-degree polynomial matrix inequality is presented, which considers more information of high-order of the time delay and more flexibility can be obtained. Second, the extended reciprocally convex inequality and some integral inequalities are used to derive a tight upper bound of the Lyapunov-Krasovkii functional derivative. As a result, some less conservative$H_{\infty }$state estimation results are obtained to design suitable state estimator gains. Finally, simulation results are provided to verify the advantage of the presented method.