Delay-distribution-dependent non-fragile state estimation for discrete-time neural networks under event-triggered mechanism

In this paper, the event-triggered non-fragile state estimation problem is considered for a class of uncertain discrete-time neural networks with random delays and state-dependent stochastic disturbances. The norm-bounded uncertainty goes into all the parameters of the addressed neural network model. A Bernoulli stochastic variable is introduced to characterize the probability distribution of the occurrence of the time delays. The phenomenon of estimator gain variations is considered to account for the potential implementation error of the state estimator. An event-triggered mechanism is proposed with hope to mitigate the network load and orchestrate the bandwidth utilization. We aim to design a non-fragile state estimator such that the error dynamics of the neuron state estimation is globally asymptotically stable in the mean square. By means of the Lyapunov stability theory, delay-distribution-dependent sufficient conditions are established to ensure the existence of the desired event-triggered non-fragile state estimators and the estimator parameters are then derived in terms of the solution to a set of linear matrix inequalities. An illustrative example is utilized to show the usefulness and applicability of the proposed algorithm.