Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying delays

摘要: This paper investigates delay-dependent robust exponential state estimation of Markovian jumping fuzzy neural networks with mixed random time-varying delay. In this paper, the Takagi–Sugeno (T–S) model representation is extended to Hopfield delays. Moreover probabilistic delay satisfies a certain probability-distribution. By introducing stochastic variable Bernoulli distribution, time delays transformed into one deterministic and parameters. The main purpose estimate neuron states, through available output measurements such that for all admissible delays, dynamics error globally exponentially stable in mean square. Based on Lyapunov–Krasovskii functional analysis approach, several estimators T–S can be achieved by solving linear matrix inequality (LMI), which easily facilitated using some standard numerical packages. unknown gain determined LMI. Finally examples are provided demonstrate effectiveness proposed method.