Augmented Lyapunov approach to H_∞ state estimation of static neural networks with discrete and distributed time-varying delays

This paper deals with H_∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H_∞ state estimation is proposed to estimate the H_∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov–Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H_∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.