Delay-dependent robust asymptotic state estimation of TakagiSugeno fuzzy Hopfield neural networks with mixed interval time-varying delays ?
This paper investigates delay-dependent robust asymptotic state estimation of fuzzy neural networks with mixed interval time-varying delay. In this paper, the TakagiSugeno (TS) fuzzy model representation is extended to the robust state estimation of Hopfield neural networks with mixed interval time-varying delays. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delays, the dynamics of the estimation error is globally asymptotically stable. Based on the LyapunovKrasovskii functional which contains a triple-integral term, delay-dependent robust state estimation for such TS fuzzy Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving a delay-dependent LMI. Finally two numerical examples are provided to demonstrate the effectiveness of the proposed method.
state estimation hopfield neural networks lyapunovkrasovskii functional linear matrix inequality ts fuzzy model