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In this paper, we discuss the outer-synchronization of the asymmetricallyconnected recurrent time-varying neural networks. By both centralized anddecentralized discretization data sampling principles, we derive severalsufficient conditions based on diverse vector norms that guarantee that any twotrajectories from different initial values of the identical neural networksystem converge together. The lower bounds of the common time intervals betweendata samples in centralized and decentralized principles are proved to bepositive, which guarantees exclusion of Zeno behavior. A numerical example isprovided to illustrate the efficiency of the theoretical results.

Centralized and Decentralized Global Outer-synchronization of Asymmetric Recurrent Time-varying Neural Network by Data-sampling