Further result on \(\mathcal{H}_\infty\) state estimation of static neural networks with interval time-varying delay

This paper considers the \(\mathcal{H}_\infty\) state estimation problem of static neural networks with interval timevarying delay. By constructing a suitable LyapunovKrasovskii functional, the singleintegral and doubleintegral terms in the time derivative of the Lyapunov functional are handled by utilizing the inverses of firstorder and squared reciprocally convex parameters techniques. An improved delay dependent criterion is established such that the error system is globally asymptotically stable with \(\mathcal{H}_\infty\) performance. The desired estimator gain matrix and the optimal performance index are obtained via solving a convex optimization problem subject to linear matrix inequalities. Two numerical examples are given to illustrate the effectiveness of the proposed method.