Abstract
This paper studies the problem of H∞ state estimation for static neural networks with time-varying delay. By construction of a suitable Lyapunov–Krasovskii functional, some improved delay-dependent conditions are established such that the error system is globally exponentially stable with a decay rate and a prescribed H∞ performance is guaranteed. In order to get less conservative results of the state estimation condition, zero equalities and reciprocally convex approach are employed. The estimator gain matrix can be obtained in terms of the solution to linear matrix inequalities. Numerical examples are provided to illustrate the effectiveness and performance of the developed method.
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