Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case
R. Vadivel, Porpattama Hammachukiattikul, Quanxin Zhu, Nallappan Gunasekaran
Abstract
Abstract This article investigates the event‐triggered synchronization problem of stochastic neural networks under passivity and passification cases. For saving communication resources, an event‐triggered approach is engaged in the design of synchronization for the delayed stochastic neural networks. To decrease network trouble, an event‐triggered scheme is suggested between the sampler and communication network. A nonfragile event‐triggered controller is intended to guarantee the finite‐time stability of the subsequent closed‐loop system. By applying the Lyapunov–Krasovkii functional (LKF) and the novel integral inequalities, a stability criteria for an interval‐time varying delay error system ensure the designed controller can fulfill the necessities of passivity and passification performance. The desired control gain and event‐triggered parameters are then found based on the linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the benefits and validity of the desired control law.