Centralized and Decentralized Global Outer-synchronization of Asymmetric Recurrent Time-varying Neural Network by Data-sampling
This work addresses synchronization issues in neural networks, which is incremental for applications in control systems or network stability.
The paper tackled the problem of ensuring outer-synchronization in asymmetrically connected recurrent time-varying neural networks by deriving sufficient conditions using centralized and decentralized data-sampling principles, with results including positive lower bounds on sampling intervals to prevent Zeno behavior and a numerical example illustrating efficiency.
In this paper, we discuss the outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on diverse vector norms that guarantee that any two trajectories from different initial values of the identical neural network system converge together. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results.