Delay-dependent robust asymptotic state estimation of Takagi–Sugeno 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 Takagi–Sugeno (T–S) 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 Lyapunov–Krasovskii functional which contains a triple-integral term, delay-dependent robust state estimation for such T–S 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.

► Robust state estimation of Takagi-Sugeno fuzzy neural networks has been considered.
► Estimator is designed to approximate the neuron states through the available output measurements.
► Discrete time-varying delays are belonging to the given interval.
► Lyapunov-Krasovskii Functionals contain triple integral terms to make less conservatism.
► Sufficient conditions are efficiently be solved by the Matlab LMI Control Toolbox.