Robust H∞ synchronization of Markov jump stochastic uncertain neural networks with decentralized event-triggered mechanism

This study examines the problem of robust H-infinity synchronization of Markov jump stochastic neural networks with mixed time varying delays and decentralized event triggered scheme. We present a decentralized event-triggered scheme, which utilize only locally available information, for determining the moment in time of communication from the sensors to the significant controller. The jumping parameters are modelled as a continuous-time, finite-state Markov chain. By formulating a suitable Lyapunov-Krasovskii functional(LKF) and by using Newton-Leibniz formulation, utilizing free weighting matrix method, the sufficient conditions under which the proposed neural network is stochastic stable. Moreover, these stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be efficiently solved via the standard numerical packages. The effectiveness of the proposed results is illustrated by numerical examples.