Outer-synchronization criterions for asymmetric recurrent time-varying neural networks described by differential-algebraic system via data-sampling principles - Li, Ping; Liu, Qing; Liu, Zhibing (Frontiers in Computational Neuroscience, 2022)
In this paper, by setting proper centralized and decentralized data-sampling principles, we investigate the outer-synchronization of asymmetric recurrent time-varying neural networks (ARTNNs) which are described by the differential-algebraic system(DAS). By constructing different data-sampling principles, which take full consideration of information acquisition at the times t_k and t_k^i. Some new sufficient outer-synchronization criteria are obtained by using the properties of differential-algebraic equations, differential equations(DEs), and integral inequalities. Finally, a numerical example is given to illustrate the superiority of the theoretical results.