An Improved Result on Guaranteed Generalized H2 Performance State Estimation for Delayed Static Neural Networks

This paper considers the problem of the guaranteed generalized H2 performance state estimation for delayed static neural networks. The activation function is assumed to satisfy a sector-bounded function. Based on a suitable Lyapunov-Krasovskii functional and a new integral inequality, an improved delay-dependent criterion is presented such that the error system is globally asymptotically stable with guaranteed generalized H
₂
performance. Compared with some existing results, the presented sufficient condition can provide much better performance. A numerical example is provided to demonstrate the effectiveness of the proposed method.