Novel H? H ? state estimation of static neural networks with interval time-varying delays via augmented LyapunovKrasovskii functional ?
This paper focuses on studying the H? H ? state estimation of static neural networks with interval time-varying delays via augmented LyapunovKrasovskii functional. By constructing a suitable augmented LyapunovKrasovskii functional with triple integral terms and linear matrix inequality technique, the delay-dependent criteria are conferred so that the error system is globally asymptotically stable with H? H ? performance. The activation functions are assumed to satisfy sector-like nonlinearities. The desired estimator gain matrix can be characterized in terms of the solution to linear matrix inequalities, which can be easily solved by some standard numerical algorithms. Numerical simulation is given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results.
static neural networks interval time-varying delay linear matrix inequality global asymptotical stability h? h ? state estimation h? h ? h? h ? h ?