Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations

In this paper, a non-fragile sampled-data control method is used to investigate the expo-nential synchronization of neural networks with discrete and distributed delays. The oc-currence of controller gain perturbations is assumed to be random, which is described by a stochastic variable with the Bernoulli distribution. An extended two-sided looped Lya-punov functional is constructed, which efficiently utilizes available state information of the sampled instants. By using the two-sided looped Lyapunov functional and introducing suit-able free weighting matrices, a sufficient condition is derived under which the resulting synchronization-error system is exponentially stable. Then, a design scheme of the non -fragile sampled-data controller is proposed with the aid of some decoupling techniques. At last, a numerical example is provided to illustrate the effectiveness and superiority of the proposed sampled-data control method. (C) 2020 Elsevier Inc. All rights reserved.