This paper is concerned with the problem of synchronization anMysis for discrete-time coupled neural networks. The networks under consideration are subject to: (1) the jump- ing parameters that are modeled as a con...This paper is concerned with the problem of synchronization anMysis for discrete-time coupled neural networks. The networks under consideration are subject to: (1) the jump- ing parameters that are modeled as a continuous-time, discrete-state Markov process; (2) impulsive disturbances; and (3) time delays that include both the mode-dependent discrete and distributed delay. By constructing suitable Lyapuno-Krasovskii functional and combining with linear matrix inequality approach, several novel criteria are derived for verifying the global exponential synchronization in the mean square of such stochas- tic dynamical networks. The derived conditions are established in terms of linear matrix inequalities, which can be easily solved by some available software packages. A simu- lation example is presented to show the effectiveness and applicability of the obtained results,展开更多
文摘This paper is concerned with the problem of synchronization anMysis for discrete-time coupled neural networks. The networks under consideration are subject to: (1) the jump- ing parameters that are modeled as a continuous-time, discrete-state Markov process; (2) impulsive disturbances; and (3) time delays that include both the mode-dependent discrete and distributed delay. By constructing suitable Lyapuno-Krasovskii functional and combining with linear matrix inequality approach, several novel criteria are derived for verifying the global exponential synchronization in the mean square of such stochas- tic dynamical networks. The derived conditions are established in terms of linear matrix inequalities, which can be easily solved by some available software packages. A simu- lation example is presented to show the effectiveness and applicability of the obtained results,