Event-triggered passive synchronization for Markov jump neural networks subject to randomly occurring gain variations - Dai, Mingcheng; Xia, Jianwei; Xia, Huang; Shen, Hao (Neurocomputing (Amsterdam), 2019)
•This paper mainly involves three contributions compared to the previous relevant works.•First, an ETCS is adopted to improve the efficiency of data transmission when addressing the synchronization of MJNNs.•Second, the randomly occurring gain variations are fully considered.•Third, by utilizing some novel inequalities with mode-dependent matrices, some easy-to-check synchronization criteria are presented. This paper concentrates on the passive synchronization issue for Markov jump neural networks subject to randomly occurring gain variations, in which the event-triggered mechanism is employed to save the limited communication resource. Moreover, the gain variations of the controller are considered to occur in a random way, which is modeled by a Bernoulli parameter. The goal is to build a controller which ensures that the synchronization error system is stochastically stable and satisfies a passive property. By utilizing the stochastic analysis theory and convex optimization technique, some results with less conservatism are derived. Ultimately, the effectiveness and validity of the design method are illustrated by a numerical example.