Further Result on H ∞ Performance State Estimation of Delayed Static Neural Networks Based on an Improved Reciprocally Convex Inequality

In this brief, an improved reciprocally convex inequality is presented to analyse the problem of H _∞ performance state estimation for static neural networks. A tight upper bound of time-derivative for the Lyapunov functional is handled by the improved reciprocally convex inequality. Then, a less conservative H _∞ performance state estimation criterion is derived. As a result, the criterion is employed to present a method for designing suitable estimator gain matrices. A numerical example is used to illustrate the effectiveness of the proposed method.