Augmented Lyapunov approach to H ∞ state estimation of static neural networks with discrete and distributed time-varying delays
Muhammed Syed Ali, Ramasamy Saravanakumar · Chinese Physics B · 2015
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.